Hyperspectral target detection has irreplaceable advantages which can identify meticulous spectral characteristic difference between the target and background. At present, the rapid development of high spaial and hyperspectral remote sensing provides more data for target detection. Target detection accuracy is closely related to target size、spatial resolution、spectral resolution、band settings、spectral characteristics of the target and background, detection algorithms. Generally, with regard to specifically target and background, the finer the resolution is, the higher the accuracy will be. Yet the spatial and spectral resolution can barely meet the need simultaneously, for technical obstacles. A tradeoff between the two factors is needed for effective target detection and the choice of appropriate remotely sensed data for target detection has always been concentrated nowadays.
To address this concern, this paper researched on target detection mechanism, evaluated the suitability of different detection algorithms. On this basis, this paper using ground hyperspectral sensor FISS (Field Imaging Spectrometer System) data and aerial hyperspectral AVIRIS data，studied on the situations where the target was small and had a similar spectrum to the homogenously distributed or complex backgrounds. Through the down sampling processing of the high spatial hyperspectral images, the paper analyzed the relationship between the spatial and spectral resolution and the detection accuracy. And then it proposed the optimal spatial and spectral scale for target detection. This study focused on the quantification of the scale impact of spectral and spatial resolution on target detection precision. Results revealed that：
(i) RXD algorithm is applicable to small and outstanding targets, anomaly detection algorithm has a higher false alarm rate comparing to other algorithms which have prior knowledges; CEM can achieve high detection accuracy in the case of small targets; ACE algorithm can be used to homogenously distributed background which can be represented by multivariate normal distribution；OSP algorithm is sensitive to the input spectrals of the background, the results may be affected in the complex background.
(ii) With the decline of spatial resolution, the pure target to be detected turned into sub-pixel, the detection accuracy experienced three stages of descending rates: gently-dramatically-gently. The radio of the target size and the spatial resolution has positive correlation to detection accuracy. The corresponding spatial resolution before the second stage is the effective scale for detection. In the ground experiment, the required spatial resolution for camouflaged detection was about within twice the size of the target. Using AVIRIS data, the required spatial resolution for aircraft detection was also about twice the size of the target.
(iii) Suitable spectral scale is related to the spectral difference between the target and the background (significant reflection peaks distance). In the ground experiment, the reflection peaks differences, associated with the target and the background, were 20nm apart. When the spectral resolution was coarser than 40 nm, the differences of reflection peaks disappeared and the detection accuracy decreased. Using AVIRIS data, since the reflection peaks differences were 200 nm apart, the detection accuracy was essentially the same when the spectral resolution ranging from 10-60 nm.
(iv) In the appropriate range of spectral scales, the spatial resolution plays an more important role in detection accuracy. In the ground experiment, the detection accuracy changed little when the spatial resolution remained the same and the spectral resolution varied from 10 to 40 nm. However, when the the spatial resolution decreased, the accuracy had a clearly decline. The aircraft detection had the same result.
(v) To select the quite different characteristic bands or abandon the similar bands from the target and background spectral can improve the detection accuracy. Refer to the multispectral sensors, the accuracy increased when we got rid of similar bands(430 nm) in the ground experiment, and the optimal combination bands were 450-510 nm, 510-580 nm, 585-625 nm, 630-690 nm, 705-745 nm. In the aircraft detection experiment, the optimal combination bands were 450-510 nm, 510-580 nm, 630-690 nm, 770-895 nm, 2235-2285 nm, 2305-2365 nm, according to the OIF(Optimum Index Factor). In addition, removing 1200-1700 nm bands can improve the detection accuracy due to the variability of the target.
It was concluded that the quantitative analysis method and results of spatial and spectral scales for target detection would be of great significance for both data source selection and studing on other target-background combinations under similar conditions.
The physiological and biochemical parameters of vegetation has been considered as significant input parameters in the models of ecological process, including carbon cycling, nutrient cycling and rainfall interception. Due to its unique advantages, the technology of Hyperspectral remote sensing has been applied widely in the area of predicting vegetation physiological and biochemical parameters. However, it is extremely hard to obtain the image data simultaneous with high spatial, high time and high spectral resolution because the limitation of hyperspectral satellite sensors. Therefore, the issue about uncertainty in the different scale of space, time and spectrum was proposed when retrieving vegetation physiological and biochemical parameters quantitatively.
To address this issue, based on simulated and measured spectrum data, this paper was focused on the uncertainty issue caused by spectral scale and spectral mixture when estimating chlorophyll content (Ca+b) and leaf area index (LAI) of winter wheat in different growth stages. The main methods we used contains physical models and statistical analysis models of empirical/semi-empirical. Meantime, a new spectral reconstruction algorithm derived from UPDM principle was proposed in order to weaken the uncertainty, which resulted from spectral scale among different remote sensors. And then the research about uncertainty of spectral scale was further discussed in time scale. Main conclusions and results can be deduced as follows.
(1) The models for inversing physiological and biochemical parameters and the generated mechanism of uncertainty.
The models and the mechanism of uncertainty in the estimation of vegetation physiological and biochemical parameters were thoroughly reviewed and analyzed. Statistical analysis models including derivative spectrum, spectral indices, variables of spectral positions and continuum removed method, and physical models containing PROSPECT leaf model, SAIL canopy model and PROSAIL coupling model, which were used to predict chlorophyll content and leaf area index of winter wheat. These models contribute greatly to the retrieve of vegetation physiological and biochemical parameters by hyperspectral technique and also provide the basis theory supports for its remote estimates.
(2) The research on uncertainty issue caused by spectral scale in the retrieve of vegetation physiological and biochemical parameters based on radiative transfer theories.
On the basis of simulated spectrum data from vegetation radiative transfer models, traditional spectral indices and new pattern index VIUPD were selected and further compared with their sensitivities to Ca+b and LAI and uncertainty of spectral scale. It is demonstrated that index VIUPD stand first because of its high precision, stability and slight effect by spectral scale, can be viewed as the effective model in the estimation of Ca+b and LAI. In addition, followed by VIUPD, the spectral indices built by vegetation red-edge information has a comparatively good performance. This conclusion can provide the theoretical foundation to the study of improving and developing new kinds of spectral indices in the applications of inversing vegetation physiological and biochemical parameters.
(3) The research on uncertainty issue caused by spectral scale in the retrieve of physiological and biochemical parameters of winter wheat based on single-temporal measured spectrum.
The spectral indices method was introduced to analyze the uncertainty of spectral scale produced in the estimation of Ca+b and LAI of winter wheat based on various satellite sensors. Results indicate that there are lots of uncertainties of every spectral index for their retrieve performance, due to the difference of spectral response characteristics of different remote sensors. The results we also found that spectra index VIUPD established by universal pattern decomposition method showed its high precision and best stability when predicting Ca+b and LAI.
(4) The research on algorithms about weakening uncertainty of spectral scale in the retrieve of physiological and biochemical parameters of winter wheat based on satellite remote images.
The algorithm of universal pattern decomposition method which was used to process multiple and hyperspectral data with its advantage of sensor-independent, was introduced to this paper. The simulated MODIS image generated from original HJ1A-CCD2 data based on spectral reconstruction principle using UPDM algorithms. And then the difference of spectral indices and the uncertainty of retrieve Ca+b exist in original HJ data, original MODIS and simulated MODIS were analyzed and compared. It is obvious that UPDM not only possess an excellent spectral scale adaptability, but also reduces the uncertainty caused by different setting of spectral channels among various remote sensors.
(5) The research on uncertainty issue caused by spectral scale in the retrieve of physiological and biochemical parameters of winter wheat based on multi-phase measured spectrum.
The methods including variables of derivative spectrum, spectral indices, variables of spectral positions and continuum removed method were selected and help to establish six kinds of spectral parameters (SP). In the entire and single growth periods of winter wheat, we devoted to analyze the sensitivities of SP to Ca+b and LAI, and discuss the spectral scale uncertainty which existed in spectral indices itself and the inversion of Ca+b and LAI. The results showed that chlorophyll index CIgreen, height of green peak (GH), absorption depth of red band (RD) and area of red edge (REA) were found to be slightly affected by spectral scale and can be considered as the effective index for inversing Ca+b and LAI of winter wheat. In addition, the situation of uncertainty also presented certain difference when taking the diverse growth period into consideration, which demonstrate that the generation of uncertainty issue were also affected by time scale.
(6) Based on the research about spectral scale uncertainty of inversing physiological and biochemical parameters of winter wheat, we can get another conclusion that the retrieve precision was not proportional to the spectral resolution. Certain noise could be found in the reflectance results due to the effects of environment, background, instruments and other possible factors when measuring hyperspectral data. Although the vegetation spectrum got from wide-band range would lost some details about its characteristics, it can avoid or partly eliminate the noise and redundant information among the narrow bands. Therefore, the selection of appropriate spectral scales and analyze methods must be taken into consideration in the estimation of vegetation physiological and biochemical parameters.
(7) The research on uncertainty issue caused by spectral scale in the retrieve of physiological and biochemical parameters of winter wheat based on simulated and measured spectrum.
In this part, VIUPD and GH which presented the best performance in the research about spectral scale uncertainty, were selected to explore their ability in the estimation of LAI of winter wheat with the influence of mixture spectral. Under the assumption that one pixel only contains vegetation and soil, we investigate the uncertainty issue of estimating LAI under the different linear mixture patterns. Results show that the addition of soil background into pure leaf vegetation resulted in the uncertainty when predicting LAI. It can be further found that the accuracy of LAI retrieve describes a generally declining trend with increase of the proportion of soil background, but not coming into a uniform rules. Meanwhile, the uncertainty generated from measured spectrum were greater than the simulated spectrum. And there are still some difference of uncertainty under various growth periods of winter wheat. Based on different satellite sensors, the uncertainty caused by spectrum mixing of vegetation and soil were found to be diverse in the estimation of LAI.
This study provides the basic theory for the inversion of vegetation physiological and biochemical parameters by hyperspectral remote sensing techniques and also contribute greatly to the development of hyperspectral sensors. While making the above achievements, there are some disadvantages as following:
(1) The spatial resolution of MODIS is quite different from HJ. Compared to HJ, MODIS data has too low spatial resolution. As a result, surface heterogeneity will generate significant effect on the spectral reflectance of pixel when examining the uncertainty issue of spectrum scale in the inversion of chlorophyll content.
(2) Only three growth periods including jointing, filling and milky stage were selected in this paper, the time nodes are less than normal. The further study can be conducted throughout the whole growing season and covered more growth stages. And a systematic study about the uncertainty of spectral scale would be executed under the more complete time sequence.
(3) In this work, the function of Gaussian spectral response was used to simulate the spectrum data of different spectral scale based on different Full Width Half Maximum (FWHM). However, the simulated data was not able to be fully accordant with the actual remote sensors. So there are still some limitation in the results we got. The further research we should pay close attention to more typical actual sensors in order to practically investigate the spectral scale uncertainties in the estimation of vegetation physiological and biochemical parameters.
(4) Linear mixing of vegetation and soil in different proportions was to analyze the uncertainty resulted from the inversion of LAI. In fact, the combinations of surface features are often in the non-linear way and the pixel is consist of more than just vegetation or soil. Therefore, the effects of variety of feature types with diverse combinations including linear and nonlinear should be considered in the application of estimating vegetation physiological and biochemical parameters.
Aiming at the problems mentioned above, the further investigation and improvement would be done in future.
As one of the most important data source of detailedvegetation classification(DVC), hyperspectral remote sensing(HRS)data make some classes distinguishable that cannot be separated in multispectral images, due to its advantage of imaging spectrum. HRSdata therefore have widespread application prospects in identification of crop species, monitoring of invasive species and precision agriculture. However, there are also some problems of HRSdata in DVC: Firstly, classification results based on only spectral information cannot meet the application requirements, as the classes become more and more sophisticated. Secondly, the increasing amount of spectral bands requires not only a huge number of calculations but also the corresponding growth of training samples in supervised classification. Thirdly, as the spatial resolution of hyperspectral sensor improves, the applications of classification are seriously affected by the salt and pepper noise in the classification results.
The semi-supervised classification algorithmsdeveloped in recent years play a key roleinHRSclassificationwith limited samples. The semi-supervised algorithmscould perform theclassification process by usinga small amount of labeled samples and a large number of unlabeled samples. However, most of the current semi-supervised classification algorithmsof HRSdataonly take advantage of the statistical characteristics of ground feature spectrum.On the basisof full investigation of current overseas and domestic research status, this study focuses on both vegetation feature band set construction and optimizationand semi-supervised classification method based on support vector machine (SVM) for limited labeled samples, to overcome the problem that labeled samples are difficult to be acquired in hyperspectral data classification. Some classification experiments of field imaging spectral data and airborne hyperspectral data with high spatial resolution are also performed, in order to verify the effectiveness of the proposed methods of sophisticated vegetation classification both on ground and airborne scales.
The main results and conclusions of this study are presented as follows.
(i) A DVCstrategy based on vegetation feature band set(FBS)construction and optimization is proposed: besides the spectral and texture feature of original images, we add 50 spectral indices that are sensitive to chlorophyll, carotenoid, anthocyaninand nitrogencontent to the vegetationFBS. Results show that this strategy is able to effectively improve the separability between different vegetation classes.
(ii) A spectral dimension optimization algorithm ofFBSbased on class-pair separablity (CPS) is proposed. This method focuses on the separablity of different vegetation classes at different feature bands, i.e. CPS. Itpreserves the original bands, texture features and spectral index features respectively that have the largest Bhattachryya distance of each CPthrough the iteration, and calculates the Jeffries-Matusitadistance to make sure that each CPmaintains a good separabilitythroughthe spectral dimension optimization of the FBS.Then Optimum Index Factor is employed to reduce the feature bands with high correlation.This proposed method can reduce the redundant data and improve the classificationefficiency.
(iii) A spatial dimension optimization algorithm of FBSbased on neighborhood pixels' spectral angle distance(NPSAD)is proposed, considering that in general the probability of adjacent pixels being the same class is relatively high. This method can set thresholds automatically according to training samples if there are, otherwise users have to set thresholds basedon prior knowledge. From the comparison of classificationresults, the proposed method could remove the salt and pepper noise from the classification results while avoiding the “edge effect” and keeping details at the same time, which can help to increasethe classification accuracy.
(iv) A progressive transductive support vector machine method based on the discrimination of both Spectral Angle Distanceand Euclidian distance(SAD/ED-PTSVM)is proposed. On the basis of traditional PTSVM method, this methoddiscriminates the unlabeled samples by respectively calculating SAD and ED, and implements “automatic label” for the unlabeled samples according to their distances to the separating hyperplane borders. SAD/ED-PTSVMmakes good use of spectral information of HRSdata, reducing the risk of incorrectly labeling and thus the time cost of label reset later. This method also effectively simplifies the parameter set of traditional PTSVM, reducing the amount of time spent on parameter optimization and thus improving classifying efficiency.
(v) A support vector machine classification method based on active learning using spectral unmixing technology(SUAL-SVM)is proposed. This method combines spectral unmixing technology in hyperspectral study and active learning strategy in machine learning field and fully utilizes the abundance of each pixel for better classification. Meanwhile, weighting factors are set to adjust the ratio of the most homogeneously mixed pixels and the most easily misclassified pixels. During active learning samples are added according to the distinguishing complexity of class-pairs, which makes the newly added samples more targeted. This method greatly improves identifying accuracy of the classes with small distribution, acquiring higher overall accuracy with less labeled samples and reducing not only the workload of labeling samples but also the time of samples training.
(vi) The results of detailedvegetation classification experiments on ground and airborne scales show that the classification method based on vegetation FBSconstruction and optimization can increase the classification accuracy of different types of crops/weeds on ground/airborne scale and extract more complete blade/crop plots information. SAD/ED-PTSVM and SUAL-SVMcan effectively improve the classification accuracy and efficiency, reaching relatively high accuracy when there are only 25 labeled samples for each class. The results of experiments illustrate that the proposed methods in this study have great potentialand broad application prospects in DVC, both on ground and airborne scales.
In urban remote sensing, hyperspectral data is a potent supplement to traditional data sources ,such as aerial photograph and multispectral data. By using the detailed spectral information contained in hyperspectral data, fine classification of urban and man-made objects can be achieved through spectral matching techniques. Moreover, information for urban planning, urban change, environment monitoring and assessment, and corresponding social and economical information can be extracted. The aim of this dissertation is to develop methods and models for hyperspectral data processing and classification, and to use hyperspectral data for the recognition and classification of urban and man-made objects. This dissertation concentrate on the following aspects,
1. The basic principles and models for hyperspectral imaging systems, the methods and algorithms for hyperspetral data processing were studied. First, base upon the sensor and optical models the problems on sensor calibration, radiometric and atmospheric correction were studied. The various methods for reflectance conversion methods were discussed and further reduced into three kinds, the methods based upon radiative transfer theory , the normalization methods and empirical methods. The spectral matching concept were introduced. Several spectral library and tools for spectral analysis were presented. By using the empirical line method, reflectance conversion of urban hyperspectral data were made with high precision.
2. The spectral representation of urban and man-made objects in hyperspectral data were analyzed. First, the optical spectral characteristics of urban and man-made objects were studied by using spectra measured on the ground. Second, the spectral information represented in hyperspectral data were studied. Third , the comparison between high spectral resolution data and multispectral data were made. These studies indicate that the diversity and complex urban land cover types can be distinguished by using hyperspectral data.
3. A method for spectral feature extraction of urban and man-made objects was developed. Feature extraction is necessary for hyperspectral data processing because of the correlation and dispersion of discriminating information between and among the numerous spectral channels. The importance of the first and second order spectral statistical characteristics for classification were discussed. Improved CA transformation, a new feature extraction method, is developed, which is based upon the relative distance between and within class pairs. By using Bhattacharyya distance and scatter plot, the effectiveness of the feature extraction method was evaluated.
4. A detection and classification method, subspace projection and matched filtering were developed for hyperspectral data analysis. Pixel spectral can be considered as signal series, therefore, theory from signal detection can be introduced into hyperspectral data processing and classification. A “Spatially invariant, linearly additive” data model was introduced to discribe hyperspectral data. By using SD filter, orthogonal subspace projection technique and matched filters, the desired objects can be detected and further classified. Urban and man-made objects were classified using this method. The results make it clear that this method is rather efficient for hyperspectral data classification.
5. A strategy for detection and classification of urban and man-made objects were developed based on the spectral characteristics. This method classify the various land cover types hierarchically. As a testimony of the methods provided, a PHI and a HyMap hyperspectral images were experimented.
The retrieval of quantitative information of minerals has strong economic and scientific meanings, and is essential for the long term development of human beings. Hyperspectral remote sensing has unique advantage and great potential in quantitative analysis of minerals, but has not been fully exploited in practical applications. However, the primary cause is due to the uncertain factors in quantitative analysis of minerals using hyperspectral remote sensing, which includes the complex mechanism of mineral spectra, the accuracy of spectral unmixing, and the accuract extraction of mineral absorption features. To address this issue, this paper firstly systematically analyzed the mechanism of mineral spectra, and then investigated the uncertain factors in quantitative retrieval algorithms, which can be generally grouped into two classes: spectral unmixing and absorption feature extraction. As for spectral unmixing, the effects of spectral unmixing models and band position on mineral retrival accuracy were fully investigated. In the field of absorption feature extraction, a new absorption feature extraction method was proposed to retrieve more accurate absorption parameters. This dissertation will further serve as a guide for improved quantitative retrievals of minerals from spaceborne, airborne, or other hyperspectral remote sensing platforms, and pave the way to explore some important subjects using remotely sensed mineral contents, including the metallogenic prognosis, planetary geology evolution, mine environmental restoration, etc. The main conlusions and results are as follows.
(1) The mechanism of mineral spectra were systematically analyzed from five different aspects, including the mineral types, the chemical change, the physical propoties, spectral measurement conditions, and spectral mixing effect, and discussed about the correlations between these factors. Among them, spectral mixing effect is the main source and theory basis of quantitative analysis of minerals using hyperspectral remote sensing data.
(2) This dissertation summarized the concept of mixing reflectance reconstruction (MRR), and proposed a thorough method to determine the accuracies of spectral unmixing models based on MRR. The MRR error can be analyzed from 3 different dimensions, including the spectral dimension, the spatial dimension, and the total dimension. By measuring the spectra of proportionally mixed mineral powders, we were able to investigate and verify the relationship between MRR accuracy and spectral unmixing accuracy. This finding validates that when the actual fractions are not available, it is possible to estimate the spectral unmixing accuracy based on an MRR accuracy analysis.
(3) A newly developed LCR model was developed based on investigating the existing spectral unmixing algorithms and physical mechanism analysis. Existing models having typical applications related to mineral analysis, including the Linear model, the NL model, the CR model and the SH model, were also summarized. Experiments on the well-known AVIRIS data for Cuprite allowed us to evaluate the effects of the five unmixing models based on the MRR accuracy analysis. The results revealed that the LCR model yielded good results in nearly all aspects, and had considerable potential for practical application. By comparing the total MRR error of ATREM dataset and Flat Field dataset, the effects of atmospheric correction on spectral unmixing accuracy were verified, but the level of influence was different for different unmixing models. LCR model achieved the most outstanding results, which verified its great robustness.
(4) This dissertation proposed a new spectral unmixing model based on derivative of ratio spectroscopy (DRS), which can directly interpret the correspondence between the target content and variations of mixing spectra, eliminate the influence of other substances in the mixture, and extract the bands which are more sensitive to the target information. Based on this model, the effects of spectral position on spectral unmixing accuracy were investigated using mineral powder mixtures. The results indicated that the absorption features have strong influence on the spectral unmixing accuracy, and the bands near the slopes of absorption feature valleys tend to have higher accuracy.
(5) Based on the study of the mechanism of background removal methods, represented by continuum removal, a new spectral fitting method was presented to obtain the background curve, and a novel background removal method named reference spectral background removal (RSBR) was given. RSBR retains the advantages of continuum removal, and when given the reference spectral background, RSBR can eliminate the influence of unwanted contribution factor, and extract the absorption feature of target contribution factor.
(6) Based experiments on both mineral powder mixtures and airborne hyperspectral data, RSBR was demonstrated to have the following advantages in extraction of absorption feature parameters: 1) RSBR can extract accurate absorption centers and absorption widths from mixing spectrum, independent of the variation in abundance; 2) absorption depths calculated from the RSBR spectra are strongly linearly correlated to the fractions of the component of interest; 3) the spectral waveform of the specific absorption factor can be well extracted by RSBR, and by using spectral matching methods, such as SAM, the mineral composition can be identified.
Detecting changes in land cover through time using remotely sensed imagery is a powerful application that has seen increased use, as imagery has become more widely available and inexpensive. Before a time series of remotely sensed imagery can be used for change detection, images must first be standardized for effects outside of real surface change.
This thesis introduced the concept, mathematical mode, and the work flow of relative radiometric normalization. This thesis established an improved pseudo-invariant features method to normalize temporally separate but spatially coincident imagery. Using the concept of pseudo-invariant features between master-slave image pairs, spatially coincident urban features with difference thresholds are identified from images; then these features were filtered using principal component analysis, their quality control is through correlation coefficients; finally a regression equation is calculated using robust orthogonal regression to normalize slave images to a master. This improved method uses objective statistical characteristics of sampling points, overcomes the defect of subjective sampling, and improves the normalization accuracy, so as to guarantee further effective extraction of land use/cover change information.
This thesis used two sets of imagery to test the performance of the standardization process, a temporally variable image pair of the same sensor, and a temporally variable image pair of different sensors. This thesis calculate RMSE, statistical characteristics, slope of the major axis in PCA, and performed simple NDVI image subtraction, to validate the reduction of master-slave differences using invariant locations. As a result of the standardization process, RMSE showed decreases in master-slave differences, statistical characteristics including mean and standard deviation is more similar to the master image, the slope of major axis in PCA is closer to unit, and NDVI image subtraction showed decreases in master-slave differences. Also, this thesis compared the result of this improved method with other methods including image regression, histogram match, Schott-PIF, and multivariate alteration detection. According to visual inspection and quantitative evaluation parameters, this improved method has accuracy and reliability; it can sample pseudo invariant points with better quality, and eliminate the multi-temporal differences and multi-sensor differences. After relative radiometric normalization, we perform change detection using three change detection algorithms and make land cover change detection analysis.
For a long time, using traditional technology to obtain remote sensing data such as satellite-based or manned space-based fails to provide timely and effective information when it comes to sudden natural disasters or dynamic monitoring of daily tasks, which roots in its low spatial resolution, long revisit cycle, time-poor, airspace control and so forth. Fortunately, this problem has been addressed by the arise of unmanned aerial vehicle (UAV) remote sensing. However, the UAV platform will inevitably result in tilting, shaking which leads to rotation and projection deformation in the acquired image, thus geometric correction is needed before analyzing and applying the UAV remote sensing data. At present, research on the geometric correction technology of UAV onboard aerial photography and aerial multispectral camera has been sophisticated from home and abroad, while relatively few studies focuses on the geometry processing of the rotary-wing UAV platform equipped with imaging spectrometer system. This thesis proposes a set of comprehensive geometric processing procedure aimed at rotary-wing UAV imaging spectrometer system. The feasibility of this process was implemented and verified through hyperspectral data which is attained by aerial imaging spectrometer system (AISS). An initial workflow of data acquisition, data preprocessing, selection of geometric approach and accuracy assessment of rectification results was formed, which provides a reference standard for the geometry of rotary-wing UAV imaging spectrometer system. The main work of this paper can be deduced as follows:
(1) Describe the composition of the rotary-wing UAV remote sensing systems, and the flight tests was conducted using the developed AISS, which equipped with a series of comprehensive systems including data acquisition and storage to meet the high-speed transmission , massive data, and light-weight design requirements.
(2) Analyze the imaging geometric principles of imaging spectrometer system, then specific to the rotary-wing UAV remote sensing platforms, design the mathematical model to qualitatively explore the impacts of systematic and non-systematic geometric distortion on the rotary-wing UAV remote sensing image and its corresponding ground points.
(3) Propose two types of geometric correction methods according to different combinations of ancillary data sets: control points based and POS data based. In the process of control points based approximate geometric correction, analysis and comparison of two kinds of detecting techniques to eliminate error control points was done under the premise of satisfying the selection criteria and selection method to get the ground control points. The appropriate error control point detection method was chosen in accord with the characteristics of the experimental data. The results show that the proposed method can effectively eliminate the error control points and improve the accuracy of geometric correction which guarantees the precision of the constructed control point database and provides strong support for automatic extraction of control point within this region; in the process of POS data based geometric correction, two fast search methods were proposed in the indirect method for determining the optimal scan lines, and according to the characteristics of the test data, best search strategy was selected to effectively improve the search efficiency and accuracy.
With the continuous development of mineral exploration technology, the surface mineral exploration work became more and more difficult. The main direction of the mineral investigation is toward the underground. Core as the final link in mineral exploration keep the records of vertical change geological information. Without undermining core surface and destroying the core integrity, remote sensing as a new technology have macroscopical, fast and informative advantages. It is an indispensable means of geological prospecting. Hyperspectral remote sensing technology has the ability to identify the different minerals and composition according to the different minerals have different spectrum feature. Geological experts can use remote sensing geological information to analysis the geological metallogenic conditions, find prospecting areas and delineate target goals which providing good information to carry out detailed geological work.
The goal of this paper is design the core imaging spectrometer data catalog system including the interface and system function design. Firstly, in the condition of full consideration of the needs of users, the core catalog need stable, efficient, accurate, etc. This paper make a detail design on the core imaging spectrometer catalog system. Also this paper explore the spectral characteristics of rocks and minerals and study the key technologies of mineral identification. Then this paper work on the detailed design of the core imaging spectrometer system catalog fully considering three aspects: user habits, convenient and elegant interface master the software. The main results and conclusions are as follows:
1. Analyzed and summarized the common spectral characteristics of altered minerals. On the knowledge of various mineral spectral information, this paper studied the hyperspectral data extraction methods, such as the minimum distance matching, spectral angle matching, spectral absorption index etc.; Evaluated the spectral quality of core imaging spectral data using these methods, and investigated the accuracy of spectral matching technique. Quantified the spectral characteristics of minerals and used the mathematical methods in calculations to achieve unknown targets. Solved the core mineral identification problem, rapidly realized the translation and analysis of core information.
2. Based on the analysis and summarize the spectral characteristics of common alteration minerals, this paper explore the spectral information enhancement and various mineral extraction methods, such as spectral derivative, envelope removal, the minimum distance matching, spectral angle matching and spectral absorption index. Using those methods to make comprehensive statistical analysis of imaging spectrometer data core, the spectral characteristics can be quantified with the method of mathematical calculations to achieve the goal of determining the unknown targets. It solve the identified problems of the mineral core and achieve quick deciphering and analysis.
3. This paper amply design the interface and function modules of the core imaging spectrometer catalog system. The interface is divided into six part menu bar, toolbar, drilling display window, core catalog window, mathematical analysis window and parameter setting window. And design detailed function of each part to meet the basic need of each catalog functions. Also the interface strive to reasonably arrange space and make beautiful interface. From the user point of view the function module was design to automatically catalog to achieve the function of import of data, automatic cutting, automatic interpretation and editing and other geological data.
The distribution and types of the minerals and rocks on the lunar surface is the main topic of the lunar exploration and it is helpful to investigate the origin and evolution of the moon. Hyperspectral remote sensing is effective to the exploration to the lunar minerals and rocks because of its fine spectral information and its dominant role in mineral identification.At present, the imaging spectroscopy has been widely used on the earth and the Mars and the mineral identification based on the imaging spectroscopy is mature, while the importance of the imaging spectroscopy to the lunar exploration has been recognized recently. Several hyperspectral instruments has been carried on the lunar satellite such as M3, HySI, SIR-2 carried on “Changdrayaan-1” and the Sagnac Imaging Fourier Transform Spectrometer carried on “change-1”.A lot of hyperspectral remote sensing data has been aquired by the Sagnac Imaging Fourier Transform Spectrometer which was first used in the lunar exploration.While the lack of the quantitative analysis on the spectral characteristic of the lunar soil reduces the precision of the lunar soil detection and limits the population and application of these aquired data in some sense.Therefore, studying the basic issues and the difficulty of the lunar soil detection can improve the detection precision. The goal of this paper is to investigate the theories and main algorithms of the lunar surface quantitative detection.
The main contents and research results of this paper are as follows:
(1) Based on the corresponding background information, the spectra producing mechanism of lunar soils was studied. The visible- near infrared and thermal spectral characteristics of the lunar surface minerals was inducted and analyzed using the spectral data achieved by the lunar satellite, the spectral data measured in the simulated library environment, the data observed by the earth-based telescopes.
(2) The quantitative analysis was made on the factors which may affect the spectral characteristics, such as the physical and chemical components, mineral size, the lunar surface temperature and the outer space environment. Then, the model of these factors to the spectral characteristics was built.
(3) The physical and chemical methods were applied to simulate the lunar soil using minerals on the earth and the spectral of the simulated lunar soil was measured by ASD spectrometer and Nuance multispectral imaging system.At the same time, the spectra were simulated according to the Radiative Transfer Equation.At last, these two spectral simulation methods were compared.
(4) Based on the calibration results and lunar surface illumination charcteristics, the absolute reflectance retrieval method was put forward to and several relative reflectance retrieval methods were used to the lunar image data and the retrieval results were compared. The results indicated that the Flat Field method is the best method to the lunar image data .Then, according to the radioactive transfer procedure, the emissivity retrieval was explored.
(5) In this paper the lunar surface mineral mapping was performed based on the laboratory measurements of the lunar samples spectra, the Clementine image data, and the emission spectral data.
The results indicated that the support vector machine is best to the surface features classification for the lunar spectra.Of all of the spectral matching methods,Spectral Information Divergence can detect the minimal difference between the silimar spectra of different types and different chemical component.The relationship between the laboratory measurements of the spectra and mineral and chemical component and the Clementine relative image was built to perform the TiO2 and FeO content retrival and was successfully used to the 10S015 image.The retrival results based on the emission spectral data indicated that the position and depth of the Christensen features can be used to retirive the mineral and chemical component, while the stability of the built relationship should be studied further.At the same time, the parameters setting suggestions for the next hyperspectral machine were brought forward according to the experimental results.
(6) The difficulty and the characteristics of the lunar surface minerals were analyzed and the content which was important to the lunar surface minerals detection was suggested.
This paper makes great efforts on the building of the influencing factors model, the retrievaling algorithm of lunar emissivity and reflectance and the method of lunar mineral mapping.Some suggestions on the improvement of the lunar surface minerals detection was made based on the lunar surface features in order to promote the application of the hyperspectral remote sensing in the lunar surface matters detection.
1.研究分析了数据降维及维数确定方法。首先对目前典型的数据降维及维数确定方法进行了探讨，对其中采取的思想进行了详细分析，并通过将噪声白化引入HySime (hyperspectral signal identification by minimum error)算法，提高了HySime算法在不同噪声条件下的稳定性与准确性。实验结果表明改进后的HySime与另一自动维数确定方法——NSP (noise subspace projection)算法在不同情况下所得结果有很好的一致性。
Imaging spectroscopy combines the spectral signatures of materials with the spatial distribution and geometric characteristics of objects, it opens a new view for us to analyse the geometric and physical characteristics of the objects. However, owing to low spatial resolution of the sensor and the presence of intimate mixtures in the scene, the signals acquired by the sensor are actually mixtures of the spectral signatures of materials that present in the scene. Mixed pixels are a major source of inconvenience in application. Spectral unmixing provides an important approach to solve the problem, and it becomes a major method of information extraction in hyperspectral data.
In the application of spectral unmixing, the convex geometry characteristics of hyperspectral data in the Feature space can be used to develop fantastic algorithms as well as be a great tool to understand and analyze the data itself. This paper mainly focus on the relevant issues and key technologies involved in the application of convex geometry in the spectral unmixing，discussing the problems in spectral unmixing based on a systematical summary of typical algorithms using convex geometry concepts. The specific research reflects in the following areas：
Firstly, it analyses the method of data dimensionality reduction and how to determine to dimension. It develops a study in the typical method of these two aspects and analyses the thoughts it adopts. I improve the robustness and accuracy ofHySime(hyperspectral signal identification by minimum error) algorithm in the different noise condition through introducing the noise albino into HySime. The experimental result shows that the improved HySime and another automatic algorithm NSP (noise subspace projection) are in good agreement under different circumstances.
Secondly, a optimization method using minimum circumscribed simplex analysis concepts is proposed. The method adopted is removing the pixels which don’t affect the final result of analysis, by introducting the directional distance, the pixels which outside the initial simplex can be found out, as a result, reducing the calculation and improving the efficiency of the algorithm.
The method that solving nonlinear constrained optimization problem directly to get the minimum circumscribed simplex is proposed and it has been proved to be feasible and effective. It can be seen as a optimization problem with two nonlinear inequality constraints. Both Sequential Quadratic Programming (SQP-MinV) and Trust-region methods can be the answer to this problem. The result shows that the minimum circumscribed simplexs achieved by these two algorithm are in good agreement and consistent with the true value, and shows a higher efficiency in SQP-MinV. Compared to other typical algorithms using minimum circumscribed simplex concept, SQP-MinV also has good performance in terms of efficiency and accuracy.
1.研究分析了数据降维及维数确定方法。首先对目前典型的数据降维及维数确定方法进行了探讨，对其中采取的思想进行了详细分析，并通过将噪声白化引入HySime (hyperspectral signal identification by minimum error)算法，提高了HySime算法在不同噪声条件下的稳定性与准确性。实验结果表明改进后的HySime与另一自动维数确定方法——NSP (noise subspace projection)算法在不同情况下所得结果有很好的一致性。
From the beginning of remote sensing, imaging technology has advanced in two major ways: one is the improvement in the spatial resolution of images; another is the improvement in the spectral resolution of images. Conventional multispectral scanners record up to 10 or so, spectral bands with bandwidths on the order of 0.10μm in visible to short wave infrared bands. Furthermore, hyperspectral imaging, or called imaging spectrometry, can acquire images in hundreds of registered, contiguous spectral bands such that for each picture element it is possible to derive a complete reflectance spectrum.
Hyperspectral remote sensing effectively make the spectral feature and geometric characters of objects together. From the view of earth observation from space, hyperspectral data provide human being more abundant information, not only in the deep explorations of object’s physical and chemical characters, but also in the precise classification of different objects and knowledge innovation. In case of so much spectral bands and such huge quantities of data, some conventional data processing methods cannot play good roles. Aiming at the hyperspectral image cube, the understanding and data processing in image spatial dimension must be changed to that completed in the spectral dimension.
This dissertation is just concentrated on above aspects and evolved in the systematic and innovative views. This dissertation begins from the introduction on hyperspectral remote sensing technology. In the second and third chapters, two key points in hyperapectral data processing and analysis area, hyperspectral data calibration and parameterizationand, and hyperspectral image classification and identification, were dissertated. The fourth and fifth chapter pays more attentions to the hyperspectral data mining supported by the temporal and spatial information. In general, this study has some advantages as follows:
(1) As for spectral feature selection, spectral bands selection and objects quickly finding in image cube were provided. On the other hand of spectral featureextraction, several selections of spectral parameterization were also provided. Considering the hyperspectral geological remote sensing, stratum spectral histogram was established specially for 14 strata in Tulufan anticline.
(2) After discussion on the traditional image classification, a new method, Expert Decision Classification Based on Feature Optimization, was provided here. It is designed out in accord with two principles: one is the spectral feature optimization and parameterization, another is fuzzy and expert decision in pixel identification. Comparing with other method, this method can acquire more accurate classification results.
(3) Several spectra of man-made camouflage materials were provided here. In the SWIR, the position and relative intensities of the major absorption features associated with water are difficult to duplicate due to the complex architecture of vegetation. In addition, convex geometry projection was successfully used in the different metal material detection.
(4) On the bases of vegetation spectral analysis and hyperspectral vegetation indices, Multi-temporal Indices Image Cube was put forward and used in the dynamic growing analysis of Japanese lettuce, Chinese cabbage, and wheat stressed by nitrogen or water contents.
(5) In the area of hyperspectral data analysis supported by spatial information, Four application aspects were provided: spatial fusion based spectral reversion, hyperspectral data analysis associated with pixel position analysis, spectral unmixing and classification in the field patch units, and image classification supported by digital geomorphology model.
【Key words】 the vegetation index time series of remotely sensed data, Harmonic Analysis, HANTS, number of frequency, outlier detection
地面成像光谱系统有着极其广阔的应用前景，而相关硬件研制、系统开发和应用研究在我国尚处于起步阶段。基于这种系统研发与应用研究都严重不足的现状，本文立足于我国第一套地面成像光谱系统（Field Imaging Spectrometer System ,FISS），主要完成两项系统性的工作：其一，在系统研发与评价方面，完成了对地面成像光谱辐射仪的数据定标、数据预处理以及测量规范的初步探讨，系统地评价了其各项性能和特点，为促使其向系统化、集成化方向发展开展了基础性的工作；其二，在系统应用与推广方面，围绕植物信息提取这个主题进行了探索性研究工作，特别是对植物“精准”信息的提取，主要从植物精细识别（类别信息）、植物生化参量反演以及植物对环境因素的生理反应状况三个方面开展了详细的研究，为植物信息的快速获取提供了新技术和新方法，同时也为地面成像光谱系统的推广使用提供了示范研究。论文的主要研究内容和成果如下：
Field imaging spectrometer system spans a very broad range of applications. However, sensor design, system development and application researches are all at the very beginning stage in China. To promote both of development and application of this kind of system, based on Field Imaging Spectrometer System (FISS) which was firstly self-developed in China, this dissertation has a pioneer exlporation of this system. The study concentrates mainly on two aspects: for the development and evaluation of FISS, the research discusses data calibration, data pre-processing, operation specifications and instruction for use of FISS, evaluates the overall performance of this novel system, lays the first stone for its progressing towards systematization and integration. For the application and popularization of FISS, the research mainly focuses on plant information extraction especially at the respect of precise information, namely, plant precise identification and classification, biochemical parameters estimation and physiological reaction adapting to change of environmental factors, presents new technology and methods for quick acquisition of plant information, and thus provides a demonstration research for the wide-use of this system in future. Main contents and results are summarized as follows:
1. The first self-developed field imaging spectrometer system (FISS) is introduced in detail, including its imaging principle, structural design and technical parameters. Spectral calibration, radiometric calibration and data pre-process such as noise estimation, noise reduction, reflectance calculation and so on, are also discussed. Based on numerous field and indoor experiments, operation specifications and instruction for use of this novel system are summarized as well.
2. Under outdoor conditions, crop-weed discrimination is explored and studied using FISS, two discrimination models Linear Discrimination Analysis (LDA) and Support Vector Machine (SVM) with different features (spectral bands and wavelet coefficients) are compared. The results show that good discrimination accuracies could be obtained using several spectral bands and those bands located in ‘red edge’ range have prominent discrimination performance. For different models, SVM with V nonlinear trait is superior to LDA especially in weed-crop multi-classes discrimination. For different features, wavelet coefficients show better performance when the amount of variables used is small (e.g. less than 10), but the difference becomes negligible when amount of variables increases.
3. Biochemical parameters are estimated based on FISS, four models, namely, Linear Regression (LR), Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR), were compared. No prominent difference was observed between estimation accuracies using different models. Features impose much influence on estimation accuracy than models. To estimate chlorophyll contents of leaves, only several key spectral bands are needed and a multiple linear model could be competent.
4. Image information could be a good indicator of chlorophyll contents of leaves. Fusion of image information and spectral information could improve precision of chlorophyll contents estimation further. Due to its unique mode of data acquisition and trait of combination of image and spectral information, FISS could be employed to estimate chlorophyll contents more accurately. Estimation error using data from FISS is 30%-40% lower than error using counterpart data from non-imaging spectrometer.
5. This dissertation studies spectral changes of plant responding to natural illumination changes. This kind of changes could be used as indicators of plant physiological status. Photochemical Reflectance Index (PRI) shows a pattern of lower values around noon and higher values during morning and dusk. This corresponds to mechanism of plant heat dissipated (xanthophyll cycle), and is consistent with Light Use Efficiency (LUE) and Photosynthetically Active Radiation (PAR) changing patterns. This result indicates that plant heat dissipation information could be detected from spectral changes using FISS data.
6. Solar induced fluorescence information was successfully extracted from FISS data based on Fraunhofer Line Depth (FLD) method using 760nm O − A 2 absorption band. Fluorescence image spectrum with high spatial resolution was obtained and showed abundant information of spatial details. Fluorescence presents an ideal pattern of higher values around noon and lower values during morning and dusk. Both spatial distribution and temporal variation of Fluorescence are in consistent with physiological changes of plants. Compared to spectral indices, Fluorescence is much sensitive to LUE and thus could be a better indicator of LUE. This result may be directly used for researches of plant physiological status and its change responding to environmental stresses, may be widely used for crop yield estimation, Chinese medicinal materials production, environmental stress detection and monitoring, plant pests and diseases and many other application fields. This study is also beneficial to the development of passive remote sensing on airborne or spaceborne platforms to detect fluorescence of plants.
7. The phenomenon of blue shift of red edge of plant leaves during dehydration process is successfully detected using FISS data and visualized in the form of image series.
8. This dissertation evaluated and assessed the whole performance of FISS from researches of three respects on plant identification, biochemical parameter and physiological change. All shows that FISS has good spectral and radiometric properties and could be used in quantitative researches and precise information mapping.
With the rapid development of spaceborne and airborne sensor and ground imaging spectroscopy, hyperspectral remote sensing image data acquisition has become increasingly frequent and the amount of data is also increasing. How to effectively manage and share a mass of spectral data is a key challenge to the remote sensing scientists. In this paper, hyperspectral data collected by the self-developed ground imaging spectral radiation measurement system (Field Imaging Spectrometer System, FISS) is the main data source to be studyed. In order to establish a spectral database system, the following two things are should to be carried out: First, the overall design of the hyperspectral database, FISS hyperspectral data access technology as well as the display of the spectrum. Combined these with the theory of multi-dimensional database based on three-dimensional spherical system of , we also come up with hyperspectral data query, retrieval and visualization. On the basis of these studies, a data format called *.mdh is proposed for hyperspectral management, and a hyperspectral database system integrated some simple spectral analysis methods and information extraction methods based on image data is devloped. Finally, a spectrum mixing analysis based on weight was presented on this paper.
In this paper, the main results and conclusions are as follows:
1. According to the characteristics of the FISS hyperspectral data and system application requirements, we designed the Hyperspectral data storage model, in which GeoRaster is as the core object. With this storage model, physical database design and detailed design is also been carried out. Research have showed that this model is useful to the the storage and display of hyperspectral database system data, and provides effective support for spectral analysis.
2. Combined with the FISS data storage model, the data storage methods based on the stored procedure and the data acquisition method based on dynamic data table conversion is used to implement the hyperspectral data storage and read. On the basis of these data structure, we proposed a new fomat for hyperspectral data management – *.mdh. This data structure can be used for multi-source data storage, retrieval and three-dimensional spherical system visualization, which is the manifestation of hyperspectral data’s spectral and image.
3. The hyperspectral database system, in which the Fiss data is the main data source is deveoped. This system have implemented the data import, query and visualization. Using the database system we attempt to built a FISS sample database and integrate some simple spectral analysis method and hyperspectral information extraction module in it. Finally, a weight spectral unmixing method based on a FISS spectrum sample library is proposed, and it is supportive for further data filtering.
Hyperspectral remote sensing image is susceptible to atmospheric scattering and absorption effect, which causes distortion in the spectral information of the surface recorded by hyperspectral sensor, leading to the fake spectral shape for the surface. The atmospheric effect must be removed in order to get the true surface reflectance. In this study, by following the principle of solar radiation transfer in the atmosphere and the characteristics of hyperspectral remote sensing images, the atmospheric effect within 7 EO-1 Hyperion images between 2001 and 2010 was corrected and the corresponding surface reflectance cube images were obtained. The main contents and results are summarized as follows:
1. A sensitivity analysis for hyperspectral remote sensing atmospheric correction was implemented and a general purpose multi-dimensional look up table was built up according to the sensitivity analysis results. As the look up table can be widely used in atmospheric correction, so the usage and interpolation for multi-dimensional look up table is described in this thesis.
2. Based on the features of EO-1 Hyperion image, a preprocess workflow before atmospheric correction was built up. The image quality was significantly improved after preprocessing and the retrieval error due to the quality of the data was reduced. The preprocess workflow in this thesis is useful as a reference for other hyperspectral remote sensing images.
3. The improved Dark Object method was applied to retrieve aerosol optical thickness and the affected factors, such as aerosol mode, surface elevation and water vapor content, were analyzed. These factors may introduce errors to aerosol optical thickness result. The aerosol optical thickness retrieval result showed good linear relationship with AERONET observed data/MOD04 aerosol optical product with R 2 equaled to 0.79, while root mean square error was found 0.03074.
4. The water vapor absorption and non-absorption channels near 940 nm water absorption region were chosen for water vapor retrieval. The water vapor retrieval result has a good linear relationship with MOD05 water vapor product. The R 2 is 0.973 and RMSE is 0.0824 g/cm 2 . The surface elevation and central band shift in water vapor absorption band has a greater impact on water vapor retrieval result than aerosol optical thickness.
5. An integrated surface reflectance retrieval model was built up under the support of aerosol optical thickness and water vapor retrieval result. The spectral shape of the retrieved surface reflectance is very similar with the theoretical one. The retrieved surface reflectance is comparable with the output of FLAASH software. Comparing to the FLAASH output, the retrieved result has an average correlation coefficient higher than 0.9, average spectral angle less than 0.08 radians and the root mean square error less than 0.02. Both the FLAASH output and retrieved result are run by the same input parameters.
6. The solar induced chlorophyll fluorescence intensity is extracted form EO-1 Hyperion remotely sensed images with the value of 1~8 W/(m 2 ∙sr∙μm), which is also close to the values calculated by other scholars. The NDVI index can be used to indicate and distinguish the Earth’s surface vegetation coverage but it has less relationship with biochemical parameters between vegetation photosynthesis and plant canopy reflectance. Due to the impact of lutein off epoxidation, the surface reflectance values of vegetate area are decreased while the non-vegetation area reflectivity will not be affected by this cause, resulting PRI photochemical index value of the vegetation area is lower than non-vegetation area. The fluorescence intensity of the vegetation area is increased successively in the following images, 2004-10-11, 2004-08-31 and 2010-06-20. The released fluorescence intensity from the golf course grass was found higher than other vegetation area, which might be
related to the type of vegetation and canopy structure.
The development of precision farming urgently requests that remote sensing technique offers to timely and accurate ground information. Soil water content, soil organic matter content, soil roughness and soil texture etc. are very important information in precision farming. As hot point and frontier in remote sensing, hyperspectral remote sensing technique not only has the advantages of traditional remote sensing that can timely and undisturbedly be used to detect large area crop, but also has special advantages. It has very high spectral resolution. More delicate spectral difference of crops can help us to precisely classify crops types and to monitor and analyze crops’ vigor and the environment factors that affect crops’ product. Hyperspectral remote sensing has great potential of quantitatively retrieving for objects’ characteristics.
This thesis focuses on extracting soil information from hyperspectral data, and puts great emphasis on the study of retrieving soil characteristics from laboratory spectra. The first chapter mainly introduced the background of hyperspectral remote sensing and precision farming, and then, introduced the applications and perspectives of hyperspectral remote sensing in precision farming. In the second chapter, we primarily introduced the measurement of soil characteristics and soil spectra in laboratory, and analyzed feature of soil spectra. The third chapter is the most important part of this thesis. We discussed soil spectral properties. It included: 1) The relationship between soil minerals and soil spectral reflectance; 2) The relationship between soil color and soil spectra as well as inversion of soil color from spectral reflectance; 3) The relationship between soil surface moisture and soil spectral reflectance as well as evaluation of several inversion method of soil surface moisture from reflectance; 4) The relationship between soil organic matter and soil spectral reflectance as well as inversion of soil organic matter and soil spectral reflectance; 5) The relationship between soil texture, soil ferric oxide and soil spectral reflectance. The fourth part studied the BRDF properties of soil and with two models inverse models’ parameter of soils. The fifth part introduced the imaging mechanism of remote sensing and the spectra and radiance calibration methods for remote sensing images, as well as inversion of soil characteristics from airborne remote sensing image. The sixth chapter summarized the whole thesis and listed the achievement of this study, as same as, pointed out the difficulties in precise inversion of soil characteristics from hyperspectral image.Main development and conclusion as follows:
(1) By analyzing a large number of soil spectra, we found except at the obvious absorption position, the line of these points’ reflectance at the wavelengths 400, 600, 800, 1350, 1800, 2100 and 2400 nm are fitted well with spectral curve. This is useful for soil spectral data compressing and band selecting.
(2) From the correlation between soil spectral reflectance and soil color, we utilized regression model to forecast soil Munsel properties.
(3) The relationship between normalized soil reflectance and moisture was investigated.
For all the wavelengths and all the soils, results show that for low soil moisture levels, the reflectance decreased when the moisture increased. Conversely, after a critical point, soil reflectance increased with soil moisture. For some soils, the reflectance of the wettest conditions can overpass that of the driest conditions. For both low and high soil moisture levels, and the seven wavelengths selected, the relative reflectance was strongly correlated with moisture. Adjustment of the relationships over individual soil types provides better soil moisture retrieval performances.
(4) The normalization of reflectance approach, derivative approaches and the difference approaches were used to forecast soil surface moisture. And the best overall retrieval performances were achieved with the absorbance derivatives and the difference of absorbance.
(5) By analyzing the relationship between soil organic matter and soil reflectance, we forecasted soil organic matter and verified the performances of models.
(6) By using BRDF model we analyzed the BRDF property of different soil at different moisture.
(7) Retrieved soil characters from hyperspectral image and developed the soil characteristics map for precision farming.
Vegetation information extraction is an important content of remote sensing application. Vegetation information extracted through remote sensing image has been used in the application of many fields, such as running bio-ecological model, vegetation sort mapping, monitoring disaster, and so on. Vegetation information extraction model is usually built by remote sensing ground synchronous experiment, in order to improve the veracity and applicability, more ground experiment data are needed to check out and adjust some existing models of vegetation information extraction. Because of new remote sensors’ continual invention, the vegetation information extraction model prepared with new remote sensors should also draw the existing ground experiment data and the existing model. The diversification of remote sensing data enlardges difficulty in model generalizing and model modificatio, while, the features of remote sensing data are inseparable to those of remote sensor’s imaging process. Then, study on the influence of remote sensor imaging process to vegetation information extraction is an important approach of mining remote sensing image‘s features of and optimizing the existing remote sensing model.
This paper couples remote sensing imaging process model with vegetation information extraction model and studies systemically on the vegetation information extraction general modeling, data simulation with the remote sensing image features, analyzing the influence of imaging factor to vegetation information extraction and optimizing vegetation information extraction model.
At first, this paper analyzes and summarizes the methods commonly used of vegetation information extraction modeling. Because of the problem that there is strong correlation between vegetation parameters and also between vegetation remote sensing signals, Partial Least-squares Regression (PLS) method and the PROSAIL-based inversion algorithm are introduced to retrieve vegetation biophysical and biochemistrical parameters. Vegetation spectral freatures extraction including spectral indices and feature selection method are also used in forest species classification. Those spectral indices can represent forest canopy structure characteristics and leaf biochemical characteristics and the classification accuracies of forest spieces in this paper are indeed improved.
Through analyzing imaging processes of optical remote sensing, mathematical modeling of those processes is accomplished. Based on the imaging process model, this paper accomplishes remote sensing data under some imaging conditions. Utilizing the simulated spectra data and the corresponding vegetation attribute, we analyze the influences of some key imaging factors including spatial scales, spectrum sampling and image geometry to vegetation parameter prediction and land cover classification.
On the basis of analyzing the imaging factors’ influence to vegetation information extraction，the conversion relation of the remote sensing features between different band widths is set up. Coupling the conversion relation with the existing model of retieving vegetation parameters, vegetation parameter predicted models can be adjusted and those adjusted models are fit to new remote sensing data. This paper studies the influences of imaging factors to the vegetation parameter extraction and brings forward the Multi-Parameters Synergy Inversion model based on the remote sensing scale factor. Through thinking about the mixed pixel involving vgeation and non-vegetation subpixel and setting up the LAI, chlorophyll content synergy inversion model based on the vegetation fraction, we bring forward the method of matching vegetation parameter inversion model according to image geometry parameter. The image geometrical model of HJ-1A multi-spectral camera is build. Model validation is carried out with ground experiment data and CHRIS hyperspectral images.
This papermakes great efforts in reducing the indetermination of vegetation remote sensing and improving the precision of vegetation classification. The study production might supply the vegetation information extraction model modeling of new remote sensor with available thoughtway and method, at the same time, it is also redound to enlarge the application scope of the existing remote sensing model and to improve the application efficiency of the existing model and ground data.
本文以自主研发的岩心组分成像光谱系统（Imaging Spectrometer System for Analying Core components）所采集的岩心成像光谱数据作为数据源，最终目标是建立岩心成像光谱数据编录系统，主要开展了以下两个方面的研究工作：一是进行岩心成像光谱数据编录系统的分析与设计。二是进行岩心成像光谱数据编录系统关键技术研究并实现岩心成像光谱数据编录原型系统。论文的主要成果和结论如下：
Mineral exploration becomes more difficult with the depth of the exploration while the cost increases drastically. Because geological prospecting techniques seem serious lag, people began to continuously find new technologies and new methods for exploring deep mines. How to give full play to the advantages of hyperspectral remote sensing technology based on drilling engineering to quickly identify deep mineralization information and realize the fine detection of deep geological structure and material composition is the current key study of remote sensing technology for mineral exploration. Using hyperspectral remote sensing technology to conduct drill core catalog and analysis of spectral features is the new research direction of information acquisition and information mining of the core. The core imaging spectrometer data catalog system is the latest stage of hyperspectral core catalog system, which uses core imaging spectrometer data to catalog and can integrate visualization of hyperspectral data’s spectral and image, and will provide effective technical support for deep prospecting.
This study aims to establish the core imaging spectrometer data catalog system based on the imaging spectrometer data collected by Spectrometer System for Analying Core components. This study includes two parts:1) to analyze and design of the prototype system of Core imaging spectrometer data catalog system; 2) to conduct Key technology research and implement the prototype system of core imaging spectrometer data catalog system.
Main results and conclusions are summarized as follows:
1. Core data storage model whose key is GeoRaster Object is proposed. Extensive researches were carried out on data imported and exported method based on procedure. The experimental results indicate that this model work well at the catalog and Visualization of core imaging spectrometer data.
2. Researches about the visualization of the Core imaging spectrometer data and the visualization of spectral curve are based on Qt and C++. The result shows that flawless combination of data obtaining method, image visualization method and spectral visualization method can support the integrated visualization of spectrum and image of hyperspectral data effectively.
3. On the basis of a successful study of logging technology,image and spectrum curve display ,visualization technologies and information extraction technology, Core imaging spectrometer data catalog prototype system was achieved .The system basically provides preprocessing, core imaging spectrometer data catalog, visualization and information extraction functions.
The process of generation, migration and transformation of non-point source pollution is simulated by means of the model of non-point source pollution，which is of theoretical significance and practical value to control and management of non-point source pollution and the protection to water sources. Based on the methodology of estimation of non-point source pollution load in the large - scale basins and the spatial datasets of Yuecheng reservoir basin, this paper studies on the coupling of spatial data and the model of non-point source pollution and proposes a method of estimation of non-point source pollution load based on pixel. The study concentrates mainly on two aspects: For inversion of the key environmental parameters, the research finishes the land use and cover classification, the inversion of vegetation coverage and the simulation of soil erosion. For estimation of non-point source pollution load, the research couples spatial data to the model of non-point pollution, accomplishes estimation of non-point source pollution based on pixel, and
estimates the non-point pollution load in Yuecheng reservoir basin. Main contents and results are summarized as follows:
1. According to the underlying surface characteristics of semi-arid areas, the decision tree classification rules based on object-oriented were established in this study. The results of land use and cover classification in Yuecheng reservoir basin showed that the overall accuracy reaches 86.95% and the coefficient of KAPPA reaches 0.8518 which meets the requirements of estimating the non-point source pollution. The vegetation coverage was inverted based on binary pixel model, and the results showed that the high vegetation coverage accounting for more than 50% of the entire basin in Yuecheng reservoir basin. Soil erosion was simulated Soil erosion was calculated based on USLE, and the results showed that the erosion modulus less than 5t/hm2 • a accounting for 73%, and erosion modulus between 5t/hm2 • a and 25 t/hm2 • a accounting for 26% of the entire basin. In general, the situation of soil erosion is not serious in Yuecheng reservoir basin.
2. The research is focus on coupling spatial data and the model of non-point III pollution, accomplishes estimation of non-point source pollution based on pixel, and the Model validation results showed that the relative error of TN, TP, NH 4+ and CODis respectively 10.11%、37.37%、28.89% and 9.76%.
3. The non-point source pollution load is estimated by the method of estimation of non-point source pollution load in the large-scale basins based on pixel in the Yuecheng reservoir basin, and the results showed that the load of nitrogen,phosphorus, ammonia nitrogen and COD which is in the dissolved state respectively is 1369.05t, 134.9t, 482.28t and1241.79t, and the load of nitrogen and phosphorus which is in the adsorbed state is respectively 531.44t and 210.45t.
P1 T 和P2T、P1和P2之间具有显著的相关性。根据回归方程对温度与比辐射率进行尺度校正结果表明，基于统计方法建立的回归方程能有效校正温度与比辐射率的尺度效应，但没有改变不同TES 算法之间的尺度效应相对大小。
Airborne hyperspectral thermal infrared data spans a very broad requirement of application. Presently, serveral developed countries carried out the application study of airborne hyperspectral thermal infrared imaging. It is of core theoretical and practical problem to study the algorithm of temperature and emissivity separation algorithms. The main data resources include two aspects: TASI data and field measurement results.The research discusses serveral algorithms of temperature and emissivity separation, and their scaling effect. Main results and conclusions are summarized as follows:
1. Based on the characters of TASI data, the basic process flowing for TASI data is built, whose main steps are radiance calibration, atmospheric correction, geometric correction, image mosaic, temperature and emissivity separation, the application of temperature and emissivity products.
2. The study of temperature and emissivity separation After the study of the algorithm of Aster_TES, alpha derived emissivity method, and ISSTES, this paper improves alpha derived emissivity, and ISSTES method combination with the TASI data, and then retrieves the accuracy temperature and emissivity.
During the Aster_TES method, the new empirical relationships between the εmin and the statistical parameters of emissivity spectrum, such as MMD, MMR and VAR are created. For TASI data, the accuracy of temperature is best using empirical relationships between the εmin and MMR, and the accuracy of emissivity is best using empirical relationships between the εmin and VAR.
The improved derived emissivity method introduced to the corrections of atmospheric effects, and Wien approximation is perfected theoretically. After analysis of the sensitivity and retrieve accuracy of improved method, the result shows that accuracy of temperature and emissivity is worse than Aster_TES method.
For ISSTES method, the accuracy of temperature retrieved by the cost function of second order difference is best, but the noise of temperature image is more than the other cost functions; the ISSTES method can only get the relatively emissivity shape, so other constraint conditions must be used for the accuracy value of emissivity.
Using for reference of good idea of Aster_TES and ISSTES methods, sunch as keeping the emissivity spectral shape and the smoothness of emissivity spectrum, the author puts forward the new algorithm named as emissivity and temperature separation based on noise separation (NSTES). This method takes the noise removing and other empirical relationship into consideration, and makes a difference for the precision of emissivity and the SNR of temperature images.
Spectral resolution is main affect factor for the pericision of TES algorithms. With the improvement of spectral resolution, the pericision of retrieved temperature becomes more and more high. When spectral resolution is less than 0.172μm, precision of temperature increases fast with the improvement of spectral resolution; when spectral resolution is higher than 0.172μm, precision of temperature increases slow with the improvement of spectral resolution. So 0.172μm can be taken as theoretical reference for the design of hyperspectral thermal infrared sensors.
Lastly, the author makes the temperature images application for diurnal Temperature range model, next work will apply the temperature to the thermal inertia, Evapotranspiration and the urban energy banlance.
3. Scale Effect This dissertation discusses four scale transformation methods: center pixels method, simple average method, point spread function method (PSF), wavelet transform method (WTM).
Center pixels method is not fit for scale transformation; the spatial autocorrelation of images using PSF method is higher than that of images using simple average method and wavelet transform method; but the standard deviation of images using PSF method is lower than that of images using simple average method and wavelet transform method. Wavelet transformation method transforms the image from spatial domain to frequent domain, and causes to error because of processing the edge pixels for each moving windows.
The implementation efficiency of average method is best, next best is PSF method, and the wavelet transformation method is worse. After analysis of the difference between T P1 and T P2 , the scale effect of temperature for city is greater than that for country; the scale effect of Aster_TES algorithm is less than that of NSTES algorithm. With increase in scale transformation windows, the histogram of difference in temperature becomes more and more scattered, and the peak value becomes lower and lower. There are significant relationship between T P1 and T P2, εP1 and εP2 . After regression analysis and scale correction, the result shows that the regression equation on the base of statistical method can decrease the scale effect of temperature and emissivity, but cant change the relative size of TES algorithm. Local variance and semi-variance are fit for scale choice. The optimal spatial resolution for hyperspectral thermal infrared is 6.25m~8.75m.
According to FAO, cropping pattern is defined as the spatial representation of crop rotations, which embodies the spatial arrangement of crops and the yearly crop sequences. Cropping pattern plays an important role for effective and controllable agricultural management. Generally speaking, rational cropping pattern decides to a certain extent whether the planter can take beat advantage of the physical constraints of soil, climate and availability of irrigation to supplement rainfall and in part a response to market opportunities. Remote sensing data with its synoptic and frequently repeated coverage provides the undoubtedly best way to obtain this information and even the case especially with the development of spaceborne high-quality imaging spectrometers such as MODIS. However, studies have been restricted to crop phenology detection or extraction of multiple cropping index based on the multi-temporal high resolution images such as TM or coarse resolution satellite derived vegetation index time series. Few studies on remote sensing cropping system can be found in the literature. The objective of this dissertation is to make an attempt to extract cropping system information from temporal profile of optimized spectral index time-series (or temporal profile).
This dissertation begins with the study on key issues related to atmospheric correction based on image itself, spectral index development to characterize the crop growth and the denoising and normalization of the temporal profile of the spectral index. On this basis, the yearly 16-day composite MODIS Enhanced Vegetation Index (EVI) time series covering North China Plain are collected. Noise-reducing and phonological feature extraction are performed on this data. A brief but reasonable decision tree based on less features is built so that one accurate agriculture-oriented land cover map is gained with an overall accuracy of 75.5％. Winter wheat and maize is extracted by the optimized features from the spectral index profile, and the typical cropping pattern map is gained.
The main conclusions are drawn as follows:
1. The model-based atmospheric correction code ACORN is quite demanding for spectral and radiometric calibration performance of hyperspectral instrument. The new spaceborne imaging spectrometer CHRIS can’t meet the requirements of ACORN at least for bands beyond 750nm.
2. An atmospheric correction flow is designed for MODIS, the key atmospheric parameters could be inverted pixel by pixel. The corrected MODIS reflectance spectrum has good consistence in shape with the in-situ measured spectrum.
3. Three algorithms are utilized to develop nitrogen-sensible spectral index based on the rebuilt airborne hyperspectral image spectra and nitrogen mapping is carried out. The nitrogen map estimated by NDVI(dr745, dr699.2) as indicator is quite consistent with the value range of the ground truth data.
4. The sensibility analysis of the popular chlorophyll spectral indices is made by the canopy radiative transfer model PROSAIL simulation. An improvement is made on the integrated spectral index TCARI/OSAVI. Validation shows that the improved integrated index is sensitive to chlorophyll for crop and forest whether at foliar scale or canopy scale. Chlorophyll maps are gained successfully for two temporal CHRIS images.
5. Choosing biomass and leaf area index (LAI) as the best indicators of crop growth, the optimal NDVI-type spectral index of MODIS is explored by simulating the MODIS bands by long term ground measured wheat spectra. It turns out, NDSI(b19,b2）, NDSI（b19,b16）and NDSI(b19,b17）all have much stronger correlations with both biomass and LAI than the two MODIS vegetation indices, while MODIS_EVI is more sensible indeed to growth indicators than MODIS_NDVI.
6. The harmonic analysis algorithm for time series (HANTS) not only can reduce the noise effects in the MODIS EVI image cube, but also can reveal the periodical characteristics implied in the temporal EVI profile. The phenological stages determined by HANTS-processed EVI profiles agree with regular observations.
7. Based on temporal analysis of MODIS_EVI profile, five phenological features are extracted to measure the calendar difference, in combination with land surface temperature to measure interaction difference of surface-atmosphere, and slope to characterize the spatial variability of land cover classes, a brief but reasonable decision tree is built to make an accurate agriculture-oriented land cover map with an overall accuracy of 75.5% , obviously higher than that of NASA USGS product. Most of all, when compared with the cropland area from official statistics, our classification shows much higher consistence with an overall mean square root error of 34.0507 kilo-hektare versus 66.1205 kilo-hectare by USGS product.
Phenological features of winter wheat and its succession maize are extracted and analyzed. Re-green stage and peak stage(heading stage) are of great importance for winter wheat extraction, while peak stage (silking stage) and the skew parameter which characterizes the accumulated biomass difference before and after silking stage are most important for maize extraction. The overall accuracy of wheat mapping is 88.38% when compared with official statistics in county unit. The cropping system map of wheat-maize, wheat-rice, and wheat-other crop is quite consistent with the rotation feasible map regarding on climate distribution. The error is caused mostly by the coarse resolution of MODIS and the great spatial variability in regional scale.
Because vegetation index time series of remotely sensed data, such as Normalized Difference Vegetation Index (NDVI) products derived from NOAA/AVHRR, SPOT/VEGETATION, TERRA, or AQUA/MODIDS, carry valuable information regarding land-surface properties in kinds of scales, they become more and more important and even one of main data sources for lots of applications including scientific researches and engineering projects. Take NDVI data set for example, various different scales of these products have been applied for detecting long-term land use/cover changes, modeling terrestrial ecosystems on global, continental and regional scales, extracting seasonal metrics of vegetation phenology to classify vegetation or land cover types, and even for estimating gross primary productivity (GPP) and net primary productivity (NPP).
However, since disturbed by cloud contamination, atmospheric variability and bi-directional effects, the vegetation index time series of remotely sensed data have serious noise. Although the most frequently-used data sets are the MVC products, such as MODIS 16-days NDVI/EVI data sets, they still include a lot of such serious residual noise. For this reason, many methods for reconstructing high-quality time-series data sets have been developed, including Best Index Slope Extraction Algorithm (BISE), Asymmetric Gaussian Function Fitting Approach (AGFF), Algorithm based on Savitzky-Golay Filtering(S-GF), Harmonic Analysis Algorithm based on Fourier transform (HAA), and so on. However, these methods also suffer several drawbacks that limit their further applications. The most serious problem for them is that the key parameters, almost for all of these algorithms, need to obtain through lots of trials which are easily influenced and brought new kinds of noise or uncertainties to the data sets by operators. Among these algorithms, because Harmonic Analysis Algorithm takes account of the physical meanings represented by the original data while fitting the curve, it associates well temporal changing regulation and spatial distribution characteristics showed on the time-series dada sets. Therefore, based on this algorithm, a new method for reducing residual noise and constructing high-quality time-series data sets for further application has been given in this paper. The new improved algorithm tries to bring less subjective noise in the data sets through the following ways:
1. Four rules have been given to guide the improvement of the algorithm: fewer disturbances to the original sets, less auxiliary data used; more consideration of the data physical meanings,good balance between effect and efficiency.
2. Outlier detection algorithm has been used to find the data points which are not proper to join the next curve fitting.
3. Key parameters automatically generating instead of obtaining by trials and experience. There are two important parameters, Numbers of Frequency (NOF) and Fitting-effect Index (Fk). The former is used to control the result of fitting, and it also reflects some phonological regulations implied in the data sets. The latter decides when to terminate the iteration.
Comparing to the HANTS result by analyzing disturbance to the data sets, detecting outlier and generating key parameters automatically, it can be concluded that the improved method has a good performance in some aspects such as fewer disturbance to the original data, while new problems,applicability of algorithm for instance, have been found which need to keep improving further.
Finally, the vegetation index time series of remotely sensed data have been applied to detect the source regions of dust weather. After analysis of the detected result and local meteorological data, two possible dust-sand sources regions of Beijing dust weather have been found. One is the junction of Inner Mongolia, Hebei and Shanxi in northwest Beijing. Another is in north Beijing, mainly is in Mengtougou country, Fangshan country and Daxing country. More attention should be paid here to find a balance between Economic and ecological benefits which will be helpful to improve Beijing dust weather.
The data of the hyperspectral remote sensing has the characteristic of combining the image and spectrum, which is the powerful supplement for the traditional data resource such as aerial picture and multi-spectral image. We study on the image information and quantitative analysis focusing on the spectral dimension because the hyperspectral data has the abundant spectral information. During transacting the hyperspectral image, the spectral mapping analysis is one of the key techniques to identify and classify the material in the image. In this paper, I aim to build a useful and effective database fitting with the hyperspectral data, then, on the base of this database, contrast and value some spectral mapping technique in common use, and then develop and validate a new spectral mapping technique. So at first this paper introduces the existing hyperspectral database briefly and its important functions, then with the standard spectrums in the existing database, do some study on the value of some typical spectral mapping technique and discuss the results of the different techniques, at last, noticed that in the hyperspectral remote sensing, the continuum removed method is very useful but used only with the spectrum of a single pixel to analyze spectrum and extract the feature bands useful with the classification, so in this paper, based on the continuum removed arithmetic, I programmed with VC++ to fulfil the functions of the continuum removed to the whole hyperspectral image, normalization and extracting the feature space for the classification. Then, aiming at the former image and the after-continuum removed image, the classification results of the MLC and SAM are compared. The results showed that the new technique is effective to identify the material with the spectrum which has the distinct absorbing and reflecting characteristic, but it’s hard to use with the material having no characteristic of the spectrum. So this method needs to be improved in the future.
In recent years, China's coastal sea level rise is faster than the global average; the upward rising trend will continue to develop in the future. Sea level rise will destroy the local ecological environment, and also it will adversely affect the social and economic development. Therefore, it is of great significance to master the sea level variation, and to forecast the trend of sea level change. In present study, the analysis of sea level change often needs dozens or even hundreds of years of data to look for patterns, most of the observation sequence is difficult to meet the requirements. GM(1,1) grey prediction model can overcome the shortage, it can predict possible future trends by digging the intrinsic relation through a small amount of data. However, using the conventional grey GM(1,1) model which is the exponential function for sea level change prediction can reflect the trend of accelerated change, but the exponential change is far bigger than the accelerated trend of real sea level change. And the single exponential function can't reflect the linear trend of sea level change, it needs to be improved.
This thesis took Yangtze river estuary area as the research area to predict sea level change. Firstly, used the long time satellite altimetry data from the T/P and Jason-1 altimetry satellites to discuss tidal correction based on the data itself and reaserch the sea level change. And also the sequence of absolute sea level changes year by year in the study area in 1993-2010 was calculated，which is used for predicting future sea-level change research. Then, in the study of prediction model, the thesis improved the conventional grey GM(1,1) model with the method of slope equal, the absolute linear improvement method, and the twice incremental linear improvement method and metabolism improvement method. Use the global average sea level change data provided by the organization of Permanent Service for Mean Sea Level as the experimental data to evaluate the improved results. Finally, this paper takes the time series of sea level change from 1993 to 2010 in the Yangtze River estuary area which is obtained by satellite altimetry data as basic data， and then uses the absolute linear improved grey model to predict the change in the future. The main conclusions are as follows： (1) The harmonic analysis method can effectively separate tidal information from satellite altimeter data. Then The results of analyzing the sea level change of time and space used the sea surface height data, from which tidal information were removed, show that: on the spatial distribution, it has two opposite trends of sea level fluctuations change at the same time from coastal to the depths in Yangtze river Estuary and its adjacent sea, and also the sea level fluctuations have north-south gradient which is related to latitude approximately; on the time scale, there exists two stable periodic oscillation signals consist of years cycle and 2 months cycle. (2) It is shown from the comparison of the results that the absolute linear improved model is the best one no matter in the data fitting effect or in the prediction of the future effect. The absolute linear improved model takes the actual situation that sea level changes always contain linear trend into consideration, it is more rationality than either the single grey model or the single linear model. The results show that the absolute linear improved grey model can be applied to the predictions of sea level change. (3) The absolute sea level of the area in 2020 will increase by33.8 mm, compared with 2010, which is similar to the research results of other researchers. And also the result of analyzing the response of sea level change on temperature show that the trend of sea level change is consistent with the change of temperature.
The determination of the coastline position is of great significance to integrated coastal zone management，and the use of remote sensing technology to extract coastline attracts more and more attention. Recently, two problems which need to be solved when using remote sensing to extract coastline are as follows: 1) The accuracy of coastline extraction cannot be guaranteed by applying single algorithm，because there are many coastal types and different types of coasts consist of different characters. 2) Due to the influence of tide and other factors, the boundary of land and water, extracted from remote sensing image directly, is not the real coastline. To obtain the real coastline, tidal correction is needed to move instantaneous waterline to MHWS (Mean High Water Springs)
The existing researches are mostly focused on the extraction of instantaneous waterline，but lacking of tidal correction and verification of accuracy. This paper presented a method combining coastline extraction with coastal type and tidal correction. MNF rotation，MNDWI，morphology and edge detectionwere applied to SPOT-4 and SPOT-5 data acquired in Qinhuangdao coastal zone to extract instantaneous waterline.
Based on the accurate extraction of the instantaneous water line, this paper also adopted the methods of using tidal data, and DEM data to calculate the slope of shoal, respectively. Moreover, the verification of accuracy of coastline extraction was achieved by the GPS data obtained in the same period. The results show that the accuracy of coastline extraction using two methods is better than the uncorrected, as a result of comparing the mean distance between GPS point and coastline extracted. The mean distance by using tidal data and DEM data is 3.48m and 6.82m respectively, which is shorter than that without tidal correction, 9.54m. Besides, the distance between the coastline extracted by using tidal data and eighty two percent of GPS points is shorter than 5m (half of a pixel), making this method more accurate.
Finally, this paper analyzed the coastline change by using multi-temporal sandy coastline data of Beidaihe which was extracted by this paper’s method. The results show that the distance of coastline moving landward is 3.30mduring the period of 2006-2009, and the distance of coastline moving seaward is 3.02m during the period of 2009-2011.
（2）在岩石矿物组分精细识别算法研究方面，比较了最小二乘不同约束模型算法反演精度，提出了基于阈值的最小二乘线性解混算法（Threshold Constrained Least Squares，TCLS），从而可以更好地反演出岩石中的矿物端元及其含量。在确定端元的情况下，TCLS模型、无约束模型（Unconstrained Least Squares，ULS）和非负约束模型（Abundance Nonnegativty Constraint，ANC）具有相同的矿物丰度反演结果，其反演精度比和1约束模型（Abundance Sum-to-one Constraint，ASC）和全约束模型（Fully Constrained Least Squares，FCLS）要高，这可能是因为在实际情况中，岩石中的矿物端元并不完备，强行将解得的矿物端元丰度和约束到1，反而会降低解的精度及可靠性。在未知端元的情况下，TCLS模型能够剔除微量矿物对端元识别的影响，具有最优的矿物端元识别能力及丰度反演精度。在一定程度上，ULS模型、ANC模型ASC模型和FSLC模型也能识别矿物端元，但其反演的端元数远多于岩石中实际含有的矿物，降低了其丰度反演的精度。
Thermal infrared imaging spectrometer has broad applications. Presently, several developed countries carried out the application study of thermal infrared imaging. Compared to the satellite / airborne platforms, field thermal infrared imaging spectrometer has more broaden applications, and its instrument development is in the ascendant.The first self-developed Filed Imaging Spectrometer System (FISS) is successfully developed in the visible/near-infrared region and gets some good application results. In order to further expand the application range of the FISS, to be applied to the geology, the plan about thermal infrared (FISS-TR) region is going to be developed.
To promote the FISS-TR on the development of rock and mineral identification, as well as to provide technical parameters of the instrument manufacturer, the dissertation carries out basic research on mineral end member and abundance determination using thermal infrared hyperspectral data. Main work and results are summarized as follows:
1. The mineral emission spectra classification principle is introduced. Based on the continuum removed, the characteristics of the emission spectra, including absorption position, absorption depth, absorption width, absorption area, and absorption symmetry, of the carbonate, sulphate and silicate minerals are quantitative studied in the thermal infrared bands. Carbonate minerals have only a very narrow, strong absorption valley, around 11.3μmin the thermal infrared band. Sulfate minerals have an obvious broad absorption features in the range 8.5～9μm.The emission spectra features of the silicate minerals are more complex than others, which different structures of silicate minerals could display slightly different emission spectra features. Because of complex Si-O vibration, silicate minerals show strong absorption features in the 8.5 ~ 12.0μminterval and have multiple absorption peaks. The results show that the emission spectra of the same type of minerals in thermal infrared bands are basically similar. It is possible to identify the rock and mineral by analysis of mineral emissivity spectral features.
2. The dissertation studies the algorithm for recognition rock and determination of mineral composition, compares mineral inversion accuracy of the least squares algorithm using different constraint model, and presents the threshold constrained least squares linear unmixing algorithm (TCLS) which could retrieve mineral end member and their abundance better. Under the condition of known end member mineral, TCLS model, ULS model and ANC model could obtain the same mineral abundance, the retrieval accuracy better than ASC model and FCLS model. This may be due to the fact that end member of rocks are not complete in reality. The abundance sum-to-one constrainet might reduce the accuracy and reliability of the results. In the case of the unknown end member, TCLS model can remove trace mineral end member obstruction, identify end member mineral and get optimal abundance inversion accuracy. ULS model, ANC model, ASC model and the FSLC model can also identify the mineral end member, but inversion end member are far more than the mineral contained in rock, as well as reduce the accuracy of the mineral abundance.
3. With combination of the measured spectra and numerical simulation spectra, the impact of the spectral resolution, signal-to-noise ratio (SNR) and continuum removal on determination of mineral in thermal infrared band have been studied, respectively. The lower spectral resolution is, the worse retrieval results of mineral end member and abundance are obtained. In the case of unknown end members，TCLS model can correctly identify the mineral end member and its model difference vary small with spectral resolution declining from 279 bands (0.021μm) to 32 bands (0.177μm). When the spectral resolution is less than to 32 bands, model difference increases fast, and results of identification of the mineral become incorrect. So, 32 bands can be taken as reference for design of FISS-TR sensor. With the SNR declining, the model retrieval error becomes higher. When SNR reaches 30, the maximum error of the abundance is less than 10%, model error is less than 5%. If the end member is unknown, SNR impacts on inversion accuracy more seriously. While SNR equals 40, it is difficult to identify the mineral end member.The results show that in order to meet the model error less than 1%, the SNR of the data could achieve 50 at least.It is important for FISS-TR to design SNR parameter. In the case of known end member, the continuum removal algorithm can be used for mineral abundance inversion. If rock spectrum is preprocessed by continuum removal, the mineral spectrum also needs to be. The maximum error of abundance is less than 2%; the model error is about 1%.
As the one of newest area of Remote Sensing technology, the hyperspectral remote sensing is used widely and widely，correspondingly the data are richer and richer. With the quantitative remote sensing is more concerned, a strong, powerful, convenient，expandable database system, which stored hyperspectral data and is combined with application function is urgent desired by the related areas. At the same time, the hyperspectral data have their own characteristic. The numbers of wavelength may be varied with the different hyperspectral equipments. One hyperspectral object can be responding to many wavelength data, image data, and many other properties. The quantities of them are great and the relation of them is complexity. And the unique characteristic of hyperspectral data is the united with the image and spectrum. Therefore the spectral database system that we have can not meet the requirements of all these characteristic.
Summarizing the results of forerunners, with the research of the status of spectral database now, according to the demands of the hyperspectral data application, this article will first define the concept of Hyperspectral Database in the world. It not only inherits the properties of spectral database system which can store and display the spectrum and other corresponding properties of objects, but also designs the data structure, stores and displays for hyperspectral data. It reflects the characteristic of hyperspectral data from the database area, makes the data structure more compact, combined the image and spectrum, and prepares for the application of data mining.
Hyperspectral Database System mainly involves several subsystems, that are hyperspectral image data sub-database, spectral assistant sub-database, hyperspectral data analyzing sub-system, hyperspectral data-warehouse with data mining, and foregrounding system. A Hyperspectral Database System prototype system was completed accompanied with these designs in this article. It includes: Spectral database based in the internet: it comes from the original spectral database system. It is advanced for it is built in the large ORACLE database platform, for it is accompanied by the data analyzing sub-system and for it is published in the internet. It will act as the assistant sub-database for the whole Hyperspectracl Database Systelm; Hyperspectral data analyzing sub-sytem based in the internet: It has basic hyperspectral data analyzing function and is built for expanding the database system applications. It will act on the varieties data of database system; Hyperspectral image data sub-database: it is the soul of the whole Hyperspectral Database system. It will realize the unique characteristic of hyperspectral data, which unites the image and spectrum in the database area. The spectrum can be extract from the image by database. This database, which combines the spatial and spectral information, will play an important role in the future; Foregrounding system: in the times of IT, using internet is the trend. Develop the foregrounding system in the internet will promote the communication of hyperspectral data and exploit the hyperspectal application. This paper will take the concept of Hyperspectral Databse System as the core, state the technology routes, basic frames, special problem in processing, how to solve them and the results in detail and introduce the comparely whole process of building a Hyperspectral Database System prototype.
At the same time, in the process of designing, solving problems and programming, this article will summarize and provide the data storing standard rules about data structure. It will also provide comparison of several data storage models under the ORACLE database platform, two of that were accomplished by this system, and state their advantages, shortcomings and choosing condition. This job will make a good technology theory basis for development of this kind system.
Finally the article makes an initial discussion of applying the data-warehouse and data mining technology in the hyperspectral data, which makes a theory discussion for integrating the data-warehouse in the Hyperspectral Database System.
Hyperspectral remote sensing is a cutting-edge technology of acquiring land surface information, with a high resolution in spectrum. With the fast development of spaceborne and airborne sensors, hyperspectral data are more frequently and conveniently received. As a result, data storage, management and effective information extraction are becoming key challenges to hyperspectral remote sensing science and technology. Especially, automatic or semi-automatic information extraction is a predictabley trend for hyperspectral researches and applications.
In this dissertation, the intension and extention of the concept hyperspectral database and spectral data mining has been brought forward after a background research on hyperspectral remote sensing and database. Based on this conception, ground-object spectra database, remote sensing image database, data mining technology have been studied, while spatial data mining and image data mining technology are focused. A conceptual design with data mining and application oriented is provided basetd upon project experience and international study. The hyperspectral database has been built up and more than 6,000 sets of spectral data have been uploaded. There are spectra of rocks, minerals, waters, concretes, trees, wheats, soils and hyperspectral images in this database. With this hyperspectral database, some researches on spectral data mining and hyperspectral image data mining have been carried out. In view of hyperspectral remote sensing technology and application, there are some advangates of this dissertation as follows:
1. Based on hyperspectral database, new storage models of spectra and hyperspectral image has been put forward. A new design has combined data, methods and models in a whole database platform.
2. To integrage different sources of spectra in one database platform, a new conceptual data structure, dualistic-core star structure, has been brought forward.
3. Forward (from spectra to attributes) and backward researches have been impleted based on hyperspectral database. Automatic optimizations have been applied in band combination and band selection.
4. From a totally different view of hyperspectral image, a projection from hyperspectral images to relation tables has been built up to improve analysis and information extraction. The following key techniques are implemented in hyeprspectral database: Band selection based on Minium Length Model, Feature extraction based on Non-negative Matrix Fraction, Data compression and decompression based on Non-negative Matrix Fraction, Object Decection based on Support Vector Machine.
Target detection and classification is one of primary tasks of hyperspectral imaging. In terms of the method of spectral expression, the style of unsupervised cluster, probability in the data, the geometrical construction of hyperspectral imaging in the band space and the it’s continuity in the image space, the dissertation draws some conclusion on feature extraction, unsupervised classification, endmember selection,
linear unmixing, target detection and anomaly detection as follows:
1. A method for spectral feature extraction was developed based on spectral recomposition. By arranging the spectra by the sort of their reflectance or DN, the spectral curves that are originally difficult to be extracted features from will usually produce some obvious features. It is helpful to feature extraction and father analysis and process.
2. The concept of spatial continuity was proposed and successfully applied to image classification, spectral optimization, redundancy reduction and real-time endmember determination.
3. A unsupervised classification method was proposed based on universal gravitation. Each pixel that was taken as a star in the universe would move with the gravitation of all the other pixels. The last formed galaxy is corresponding to the result of classification.
4. Two approaches of autonomous spectral endmember determination were developed. Based on the convex nature of hyperspectral data in its characteristic space, Gram–Schmidt Orthonormalization process, high dimensional analytic geometry and distance between pixels were introduce to find a unique set of purest pixels in an image; A new volume formula of simplex which was independent of dimension of the data was introduced to find all the endmembers which are larger than any other volume formed from any other combination of pixels. The concept of endmember constrution function was first proposed, so the weightiness of each endmember is depended on it’s influence to the simplex contruction, but not it’s information magnitude. It’s significant to the small target extraction.
5. A method of target extraction based on endmember projection vector was developed. Based on the convex nature of hyperspectral data in its band space, a series of vectors named endmember projection vector are produced for use of object extraction. The technique is based on the fact that in band space, any endmember is the farthest point from the hyperplane consisted of all the other endmembers.
6. A new theorem about the nature of simplex was proposed and applied to spectral linear unmixing. Once all the endmembers were found, the image cube can be "unmixed" into fractional abundances of each material in each pixel by a simple ratio of volume.
7. The idea of weighted sample correlation(covariance) matrix was proposed and applied to the small target detection and anomalous target detection. The general target detection algorithm with low probability was based on the abundance of image information. The method of target detection based on weighted sample correlation(covariance) matrix not only can reduce false alarm rate to small target , but can extract target with high probability.
8. A small and anomalous target detection method with low probability which can effectively suppress background and other small target was developed. because of not taking into account the influence of other small target and abnormity, the general small target detectors have high false alarm rate. The method developed in this dissertation perform effectively in extracting small target and abnormity with low false alarm rate.
9. A method of abnormal detection based on distance of pixels in the whiten image was developed. RX detector can’t find the abnormity which was in major components in the original image. After image was whiten, it’s simplex construction in the band space gradually transforms to “sphere”, the abnormal pixels lie outside the sphere, so we can find them by distances to the center of a sphere.
Satellite on-orbit test is a useful way to monitor satellite platform, on-orbit status and attenuation degree of sensors, and is the premise and base for satellite data’s processing and application. The article studies the contents and methods of small satellite on-orbit test from systems engineering in our country, which is used to direct our DMC（Disaster Monitoring Constellation）small satellite on-orbit test, and also as a reference for other similar satellite in our country.
The satellite studied in the article participates in international satellite constellation network. Compared with other satellites of our country, this satellite has its own characteristics and operation manner. The satellite is developed and launched by SSTL（Surrey Satellite Technology Limited，Surrey Space Centre University），which is handed over to China when on-orbit test period. The on-orbit test not only evaluates satellite platform, on-orbit status and attenuation degree of sensors, which provides the necessary parameters for satellite data’s processing and applications, but also the technical base for our checking and accepting the small satellite. So this requires the systematization, standardization, and internationalization of the DMC small satellite on-orbit test in our country. At same time, this job is also a part of Demonstration Center for Spaceborne Remote Sensing, CNSA, which is helpful to establish the system of our spaceborne remote sensing demonstration.
Based on above points, the article aims to establish the system of the contents and methods of our DMC small satellite on-orbit test, and also as a reference for other similar satellite in our country. We think that the DMC small satellite on-orbit test includes four aspects: geometic characteristics of sensor’s image, radiomatic characteristics of image, spectral characteristics of image, image general quality and its application potential.The evaluation of s geometric characteristics of its image includes: satellite pose, evaluation and adjustment of orbit parameters, directing precision of camera and its wavering performance, interior and exterior geometry precision of image, band matching, image resolution, and so on. The evaluation of radiomatic characteristics includes: MTF, SNR, relative radiometric calibration, absolute calibration, radiometric stability, dynamic range, the linearity of spectral response, and uniformity of CCD etc,. Because of few bands and low spectral resolution of our DMC small satellite, we only analyze band setting of sensors and change of spectral responding function qualitatively when evaluating the spectral performance of image. In addition, the article brings forward technical guideline for evaluating general image quality from data application, and analyzes application potential of our DMC small satellite image data.
The most contribution of the article is to establish the contents and method system of our DMC small satellite on-orbit test based on the characteristics and performance parameter design of our DMC small satellite, and summary of foreign similar satellite on-orbit test. It is the first system in our country that is applicable to push-broom, earth observing satellite, and has great directive significance for similar satellite on-orbit test. Furthermore, the article makes deep research in test methods, and advances some creative test methods, mainly including: a highly precise matching algorithm to test errors of bands matching; a method to calculate relative radiometric calibration.The aim of the article is to establish the system of the contents and methods of our DMC small satellite on-orbit, not an innovation of evaluation methods. Because our DMC small satellite is planned to be launched in May, 2005, it is impossible to get necessary test data. So the article is focused on experiment and analysis of some content and method of evaluation. However, the article summarizes the necessary data and documents in our DMC small satellite on-orbit, and advances the whole scheme and implementing method, which is meaningful to actual satellite on-orbit test.
Although hyperspectral remote sensing technology is advancing our understanding in various fields, it also brings us data redundancy. It is really a great challenge to manage and share hyperspectral remote sensing data effectively and obtain useful information from such massive data. We believe the establishment of a database for effective management and sharing of hyperspectral remote sensing data is really necessary. Meanwhile, the development of multidimensional database technology and multidimensional analysis technology will lay the foundation for obtaining useful information from massive data.
This study aims at the establishment of a multidimensional hyperspectral database for rock and mineral based on the hyperspectral data collected by the Field Imaging Spectrometer System (FISS).This study includes two parts:1) design and develop the prototype system of FISS Hyperspectral Database (FHD); 2) probe into the basic concepts and design of the multidimensional hyperspectral database for rock and mineral (MDHDR&M) as well as the design of multidimensional analysis tools based on multidimensional database theory.
Main results and conclusions are summarized as follows:
1. FISS data storage model which key is GeoRaster is proposed. Extensive researches were carried out on data import method based on procedure and data extract method transformed through dynamic data table. The experimental results indicate that this model and data import method work well at the integrated storage of both image and spectrum of hyperspectral data.
2. Researches about the visualization of the FISS image and the visualization of FISS spectral curve are based on Arc Engine and C#. The result shows that flawless combination of data obtained method, image visualization method and spectral visualization method can support the integrated visualization of spectrum and image of hyperspectral data effectively.
3. A prototype system of FHD with several spectral analysis methods is developed. This prototype system is composed of such functions as management, visualization and analysis.
4. This study redefines the basic concept of MDHDR&M and preliminarily designs its system structure. This model also brings in a multi-dimensional nested data structure to establish a snowflake schema. Besides, the compositions and detailed functions of multidimensional analysis tools are analyzed. Some results of multidimensional analysis are showed. All these lay the foundation for the further implementation of hyperspectral multi-dimensional database for rock and mineral.
Hyperspectral remote sensing was created by the geologist when they studied the spectral features of rocks and minerals and its application in geology is earliest and most successful. Even though, the technique still is not mature and perfect because its short in satisfying some need in modern geology application. The accurate recognition of rocks an minerals based on the diagnosis spectral features, the quantificationally extraction of geochemistry information and the extraction of small geological objects all need to deep the understanding of the mechanisms and principles of spectroscopy of rocks an minerals. The technology and method for the unique application should be developed for the improving of the geological remote sensing technology.
The art of geological study by hyperspectral remote sensing was introduced in the paper, especially the spectroscopy of rocks and minerals and its information extraction methods. Then the spectral mixing models were studied, the spectral features of altered mineral groups were analyzed and the information extraction methods were investigated. The rock and mineral information extraction method was set up for alteration recognition and several tests were practiced. At last, the target detection algorithms are studied for the recognition of alteration, the proper geological object detecting strategy was set up and was practiced for the alteration information extraction in vegetation covered area and no-vegetation area. The main content and result is introduced as follows:
(1) The spectral simulating model which integrating the Hapke model and Shkuratov mixing model was provided based on the study in the linear and nonlinear spectral mixing models. The spectral simulating result using the model provided in the paper was better than the traditional linear model and was tested by the experimentation. The approximation for single scattering albedo of opacity minerals in Hapke model was provided to get the spectral simulating method adapted to the spectral simulating of mineral mixtures which containing opacity minerals. The model is useful for the establishing of the bidirectional reflectance library of rocks and minerals and can be applied in the spectral simulating of the moon and other planets.
(2) The mixed spectral experiment was practiced which integrating the high spatial resolution camera, multispectral imaging system and ASD FieldSpec FR2500. This idea gived the supporting for the study of spectral mixing model and can be applied in the aero platform in large scale in further study. A spectral simulating model for remote sensing with low resolution was provided, it integrated the linear and nonlinear model and can used for the spectral simulating of mix-pixel such as the image spectra of alteration zone.
(3) Based on the analysis of mineral component and spectral features in typical alteration wall rock, object-oriented information extraction method was provided for the extraction of rocks, minerals and alteration, which contained three steps: the object spectra simulating, spectral feature analysis and spectral matching, weight function decision. This method was used for alteration zone in Cuprite area successfully. The method was also used for the execution of the geological spectral histogram in Baogutu area taking the spectral of alteration group as reference. The accurate recognition and mapping can be realized if the spectral resolution and signal to noise is enough high. The information extraction method which integrating of spectral matching and color component can greatly enhance the rock body and alteration in the image,despite the spectral and spatial resolution of ASTER data is low.
(4) Several target detecting algorithms with objects known and background unknown considering the characteristics of geological objects were studied, such as Orthogonal Subspace Projection(OSP), Constrained Energy Minimization(CEM), Adaptive Coherence/ Cosine Estimator(ACE), Adaptive Matched Filter(AMF),Elliptically Contoured Distributions(ECD). The principle of the detectors and the factors effecting detecting efficiency were analyzed and the improved strategy was provided. The integrated weight function correlation matrix (covariance matrix) was considered and the improved algorithms can detect the big object bedides the small object. Several target detecting algorithms were selected by the detecting test of small geological object with different background in vegetation covered area and no-vegetation area, the efficiency of ACE and AMF is best and CEM is better.
The major objective this thesis is the key technique research of the airborne multispectral digital camera system and its application on the remote sensing of oriental migratory locust. The main research works are list bellow：
1. An airborne multispectral digital camera system has been successfully developed based on area CCD sensors and other peripheral facilities. In fact, the whole system includes three different multispectral camera systems, Basler cameras with optical filter system, MS4100 high-resolution 3-CCD digital multispectral cameral and Kodak professional DCS760 camera. Those systems have their independent hardware and control software, and they can be used either in combine mode or separate mode. IEEE-1394 Bus and Camera Link technique are used to capture the image data. IEEE-1394 Bus and Camera Link are the latest advance in communications interface technology for transmitting digital data. The computer, controlled by a executable program under windows operating system, provides all control functions including exposure time control, exposure interval control, retrieving images from grabber, saving images to disk array, displaying captured images for real time monitor and recording GPS information. The GPS receiver provide 1PPS pulse signal to trigger circuit, and provide the GPS locations to the control computer as well. The control programs are developed using VC++6.0, and compatible with WINDOWS NT/2000 operating system.
2. For the first time in China, the airborne multispectral digital camera system was used to research the remote sensing of the oriental migratory locust. After analyzing the biological and environmental characteristics of the locust, the detect method was proposed that we can monitor the spectral changes of the vegetable caused by the locusts. In august 2002, a flight experiment had been done to monitor the locust area in Nandagang farmland in Hebei province. A large amount of excellent image were acquired in this experiment. And the result denoted that the airborne remote sensing method was feasible. On the other hand, the result indicated that the newly designed digital camera systems were successful.
3. For frame transfer CCD sensor, in the phase of the frame data transferring, the light source still hit the sensors surface while part of the image was moving toward the light shielded region. So the image was polluted, and we call this frame transfer smear. In this thesis, a restoration algorithm has been proposed to recover the frame transfer smear. And effect of this algorithm had been testify by many ground and flight images.
4. On the radiometric correct process of multispectral image, an in-flight statistic template correcting method was introduced. And this method was testified using 1431 multispectral images acquired in the test flight.
5. After analyzing the difficulties and disadvantages of the optical band register, image register method was adopted instead of the optical register. This is implemented by using the precise hardware synchronic trigger control circuit and the after flight image process. As a result, the camera heads do not need to be aligned at the precision of pixel level. The only requirement is that the field of view of all cameras is roughly the same. One more advantage of image register is that the replacement of different lens and optical filters would not be limited in this system. This will greatly enhance the system performance and adjustability.
The sea surface change has been a major issue with some political and economic significance. So far, many people think that the sea has a trend of continues rise. However, it is difficult to decide the change of a regional sea using the same trend for they each has a complicated change. In history, many researchers focus on the Changjiang Estuary., because the consequence of the sea change firstly take on in this area.
With an all-weather, all-time, high space resolution and precise repeat coverage, the satellite altimetry is an effective means to obtain the information of the sea surface height. The precisionof the altimetry data decides that of the information extracted from the sea surface.
In this paper, the T/P and Jason-1 satellite altimeter were combined to detect the sea change of the Changjiang Estuary. The altimetry data has some error such as the radial orbit error and the environment error. This paper studied the sea level change in the Changjiang Estuary.
An effective method was used to perform the data editing, after which 80% of the data was thought to be good data. A new method was proposed for crossover adjustment. It is easier to perform, steady and without rank defect. After processing with this method, the altimetry data had lower radial orbit error. Meanwhile, in this paper, the altimetry data of different cycles were corrected to a reference orbit using the slop gradient method. Because the T/P and Jason-1 satellite ran the same orbit for nearly 6 months, the analysis of the coherency of the two kinds of altimetry data was performed. The result was that the altimetry data of Jason-1 satellite were consistentwith that of T/P. In addition, a regional tide model was calculated because the tide model of the altimetry data was for the global area and was not fit for a regional area. The regional tide model was calculated using a 18-year time series of altimetry data and conversely was for the correction of the altimetry data. After that, a more precise time series of the altimetry data was calculated, from which the sea rise rate was calculated to be 1.65mm/yr, Moreover, the inter-annual change rule was found: the mean value of the sea height in September, October and November were higher than other months and in April there was a peak extreme value.
The atmospheric correction of remote sensing imagery for Case 2 waters is always the main problem to be solved for inland color remote sensing. Two difficulties exist therein: 1) the optical properties of the case 2 waters (especially inland waters) are more complicated and spatially unhomogeneous with more reginal charateritics than those of case 1 waters, which is hard to meet the hypothesis in the atmospheric correction algorithms for case 1 waters; 2) the aerosol optical properties are greatly influenced by human activities and surface factors above the inland case 2 waters. Moreover, the complicated aerosol optical properties and the relative large aerosol optical thickness (AOT) result in the assumptions of some atmospheric correction algorithms questionable. So, the aforementioned probems, especially the second problem was studied during the atmosphere correction of remote sensing imagery for Taihu, the typical case 2 waters.
Aerosol and water vapor are two main factors affecting the accuraccy of atmospheric correction of remote sensing imagery. The scattering and absorbing effects of the aerosol are more complicated. So, the retrieval algorithm of the AOT and water vapor column (WVC) were studied with the multi-band sunphotometer. The AOT retrieval accuracy was analyzed by the influence of spectral response functions. The absorption features were also analyzed outside the 940nm band for these gases such as H2O, O3, NO2 ,CO2, CH4 by Line-by-Line Radiative Transfer Model (LBLRTM) and the gases’ fine spectral cross-sections of SCanning Imaging Absorption sepctroMeter for Atmospheric CartographHY (SCIAMACHY). The calculation algorithms of the optical depths for these gases were improved. Also, the relationship among absorption transmittance, WVC and observation zenith angle in 940nm-band was studied by LBLRTM. As a result, a new algorithm was proprosed with high WVC retrieval accuracy, of which the relative retrieval error was no more than 0.04 even with large zenth angle (~63°). Accordingly, a new calibration method for water absorption band which is independent to the AOTs of the neighbor bands.
The above mentioned improved methods were applied to CE318’s in-situ data around Taihu from 2005 to 2007 to retrieve AOTs. Combining with the products of Aerosol Robotic NETwork (AERONET) of Taihu, the time-spatial distribtion of aerosol optical properties was analyzed and the seasonal aerosol types of Taihu were also modeled. These results were used to improve the accuarcy of atomospheric correction.
Based on these studies, it was analyzed the influences of atmospheric correction accuracy for case 2 waters by errors such as aerosol type error, AOT error and the error of geometric imaging parameters( solar zenith, view zenith, et al.). Then a general method was proposed to establish atmospheric correction look-up tables. And the atomopheric correction was accomplished for European Space Agency’s (ESA) Project for On Board Autonomy/Compact High Resolution Iamging Spectrometer (PROBA/CHRIS) and airborne Wide field-of-view Hyperspectral Imager (WHI), respectively by image itself and by in-situ atmospheric data. In order to compensate the polarized effects of gases’s scattering and aerosol’s scattering, the vector radiative tranfer model 6SV1 was used in the radiative tranfer calculation.
Aimed to image-based atmospheric correction algorithm, present algorthms of case 2 water atmospheric correction were summarized. The iterative and noniterative atmospheric correction algorithms were proposed for hyperspectral remote sensing imagery, which were the coupled ocean-atmosphere correction algorithms with look-up tables. These algorithms could retrive remote sensing reflectance (Rrs), aerosol type, AOT, water quality parameters (suspended matter concentration, chloroyphyII-a concentration and aborption coefficients of colored dissolved organic matter), et al. 6SV1 modle and the simple bio-optical modle were adopted for atmospheric and ocean radiative transfer process, respectively. After atmospheric correction for CHRIS data and compared the results with the in-situ measured Rrs of the quasi-synchronous stations, the satisfactory Rrs retrival accuarcy were abtained by both of the algorithms.
Take WHI data as a case study: the hypersepctral remote sensing imagery was accomplished for vicarious calibration based on the synchronous measured calibraion tars’ reflectance, water Rrs and atmosphere parameters. Also the valid spectra of WHI were evaluated for case 2 waters. Then the atmospheric correction for WHI was finished based on lookup table, which was estabilshed by measured atmosphere paramters. Besides, the fast geometric rectified algorithm based on POS data was proposed for refctifying the serious geometric distortion of airborne imagery, which was used to futher process the data after atmospheric correction.
The method for aerosol optical properties in this study can be applied to can extend to other regions. Also the principle of the coupled ocean-atmosphere correction algorithms fit to all the hyperspectral data with the spectra from visable to near infrared ranges; however, small modification may be made to these algorithms according to imagery band settings.
Monitoring inland water quality by remote sensing technology has the advantages of wide coverage, rapidness, low cost, and dynamic monitoring over a long period of time. However, inland waters always have complicated opticalcharacteristics, and different inland waters have different optical characteristics. Then it is difficult to build a high-accuracy and adaptable model to retrieve inland water quality parameters. Also, most models to retrieve water quality parameters neglect the bi-directional reflectance distribution of water optical field to lose accuracy. So it has great scientific and realistic significance to build retrieval model based on optical characteristics and bi-directional reflectance distribution function of water optical field.
The paper chose two typical inland waters as study area, including eutrophic water Taihu Lake and mesotrophic-dystrophic water Three Gorges reservoir. We performed 6 times experiments in Taihu Lake, collecting multi temporal inherent optical properties and apparent optical properties, WHI (Wide Hyperspectral Imager) airborne remote sensing images and CHRIS (Compact High Resolution Imaging Spectrometer) spaceborne remote sensing images. We also performed an experiment in Three Gorges reservoir, collecting inherent optical properties, apparent optical properties, and a scene of CHRIS image. Based on these data, the paper made a comparative analysis of the reason of different optical properties distribution between Taihu Lake and Three Gorges reservoir. Then, we built inherent optical properties database and apparent optical properties database in Taihu Lake and Three Gorges reservoir, as Hydrolight inputs. We simulated bi-directional reflectance of different waters and analyzed the law of bi-directional reflectance distribution function of water optical field which was also verified by our measured data. Furthermore, we built a look-up table of bi-directional factors of water optical field. Oriented to three kinds of water quality parameters concentrations of chlorophyll-a, suspended matter and CDOM (Colored dissolved organic matter), we developed retrieval analytic
model based on inherent optical properties database and bi-directional factor look-up table. At last, we used WHI and CHRIS images to retrieve water quality parameters to
validate the models we had done, and we got good results.
The research findings in this paper are also can be applied to other similar types of inland waters. This will accelerate application of remote sensing in monitoring
water quality in different lakes and rivers.
It is an important direction of international remote sensing technologies to develop aerial large array CCD digital remote sensing system. The development and digital progress of national aerial digital remote sensing technologies and photography devices will be greatly pushed about, and sharply descend the depends on foreign products of the same kind.
What’s more, the rapid improvement of China society and economics urgently requires large amount of high precision and high special resolution remote sensing images. Aerial large array CCD digital remote sensing system will just meet the requirement as it can obtain surficial information high availability.
In order to follow the currency, hyperspectral research group of Instituteof Remote Sensing Application, CAS pushes out a multi-mode airborne digital camera system, what is called MADC for short.
MADC system is combined with 3 digital cameras, which each has a large array CCD as 4K×4K. With different position and pose, the cameras can be combined to form different imaging modes, which are Wide Field, Multispectral, and Stereophotography mainly. MADC system can also be linked with Position and Orientation System (POS), and its cameras can be controlled to expose synchronistically associating with POS data according to regular time set by shutter control system. POS system can be seemed as the combination of GPS and IMU (Inertial Measurement Unit), and its data can show the spatial position and pose of aerial platform. To do geometric correction of remote sensing images aiding with POS data, it can get rid of the limitation of ground control points (GCP). So combined with normal aerial digital photographic devices, multi modes, multi spectral, and association with POS are the important characters of MADC.
This paper studies the imaging mechanism of each working mode of MADC, the calibration of MADC and the methods to correct MADC data with POS information for the fist time at home. The main contents include 7 aspects as showed below.
(1) Simplifying the geometric relationship of MADC’s cameras. By abstracting, the thesis gets the basic imaging mode of MADC and does detailed researches to the geometric of spacial projection. And the paper gets some important conclusions and inferences about MADC imaging, and even about normal mulit-angualer remote sensing.
(2) Analyzing the geometric factors, which are actually influencing imaging while MADC system working. The paper promotes interference models of atmosphere infraction, hypsography and earth curvature, and gets the conclusion that the interferences of atmospheric infraction and earth curvature are not neglected.
(3) Aiming at calibrating MADC system, the paper studies the distortion models of lens and does distortion correction of images. Basing on resection of single photo theory, the thesis gives methods to calibrate the pose of cameras’ optical axis and spacial position of pre-node of the lens by GCP and POS data.
(4) Researching how to relate POS data to image lines of linear CCD or images of array CCD, giving the relation methods for array CCD images, and resolving the problem of change mulit-center projection to single center projection.
(5) Studying the imaging effects of MADC multispectral mode and stereophotography mode, estimating the working results of optical filtering chips of MADC, and putting forward several possible stereophotography models.
To evaluate sensor design artifacts and properties, it is useful to simulate their designs using a radiometric correction ray-tracing tool. Imaging simulation of data of optoelectronic remote sensing systems plays an important role in the development, optimization, calibration, test, and application of such instruments and the interpretation of their data products. Imaging Fourier Transform Spectrometers (IFTS) are becoming popular sensors for hyperspectral remote sensing, but the image simulation of these sensors is still in its preliminary stage. In China, Imaging Fourier Transform Spectrometers are becoming the primary spaceborne imaging spectrometers. One sagnac Fourier transform spectrometer onboard Chang'e-1 is developed by Xi'an Institute of Optics and Precision Mechanics of CAS. As the first China lunar probe satellite, Chang'e-1 was successfully launched in 2007 In addition, China small satellite “HJ-1A” ,which will be launched soon in this year, is also equipped with one imaging Fourier transform spectrometer (HJY20-1-A). In China, In comparison with the rich experience in data processing of airborne dispersive imaging spectrometer，the research on data process of IFTS is very limited. So it is very urgent and important to carry out imaging simulation of IFTS for data processing.
The image simulator of sagnac IFTS is proposed in this thesis, to evaluate, demonstrate and improve the status of sagnac IFTS. The evaluation of the main technical parameters about HJY20-1-A was performed based on the simulator, and the parameters that affect the precision of recovered spectra are proposed and the improved algorithm is developed. Moreover, an integrity system is formed, which includes three functions: image simulation, parameter evaluation and algorithm improved.
This thesis consists of four parts:
1. Analysis of imaging principle of IFTS and comparison with other types. IFTS and dispersive imaging spectrometer is compared, and Michelson IFTS is compared to SagnacITFS for their imaging principle.
2. Simulation of the incident radiance of Sagnac IFTS. First, the frame for Sagnac IFTS imaging simulation was constructed. The simulation frame was composed by three parts: scene reflectance image simulation, radiative transfer simulation and sensor system simulation. Three methods were utilized to simulate scene reflectance image. They were reflectance simulation based on classification, spectral matching and analysis of spectral variability; reflectance simulation based on spectral unmixing, spectral matching and spectral mixing; interpolation-based reflectance simulation. The ultra-spectral, high spatial reflectance image was simulated by these methods and using AVIRIS airborne reflectance data and spectral library. Then, the simulated reflectance image in conjunction with 6S radiative transfer model was used to simulate the incident radiance image.
3. Simulation of the sensor imaging process of Sagnac IFTS. The total imaging process consists of three modules: spatial response, interferogram simulation and detector. The artifacts and noises of the imaging process were analyzed comprehensively. The simulation model was given to consider the noise of base image. Then, interferograms of CE-1 and HJY20-1-A were simulated and payload parameters of HJY20-1-A were analyzed.
4. Analysis of the effects of recovered Sagnac IFTS spectra. Research indicts that the accuracy of reconstructed spectrum can be improved by apodization functions, but the accuracy was not very high, even limited for HJY20-1-A. Finally, an improved algorithm based on the standardization of the instrument line shape function (ILS) was proposed, the reconstructed spectrum by this algorithm was gained with higher accuracy. The relation of the reconstructed spectra at different spectral resolution was analyzed, and the formula between the typical vegetation indices of HJY20-1-A and Hyperion was built.
Hyperspectral remote sensing has been developed in hundreds of narrow contiguous bands and may provide a wealth of spectral information for data exploitation. However, due to the limitation on sensor cost, the resolution cell corresponding to a single pixel in hyperspectral imagery often contains several kinds of distinctsubstances. This phenomenon is a little more serious than other optical data. In this situation, a great challenge in information extraction from hyperspectral remote sensing data is decomposing a mixed pixel into a collection of endmembers and their corresponding abundance fractions, namely spectral unmixing. However, the wealth of spectral information in hyperspectral data enhances the ability for spectral unmixing. The issues of subpixels and mixed pixels analysis is one of the most focused applications of hyperspectral remote sensing data analysis. The research on the mechanism of mixed pixel and the adaptability of the spectral unmixing techniques in different scale data is the key point to solve the mixed pixel problem and further to improve the accuracy of classification and target detection.
Based on an integrated workflow of end-to-end stages of spectral unmixing, this dissertation makes a study of Endmember Extraction (EE) and Independent Component Analysis (ICA) for linear spectral unmixing techniques, addresses the problems arising in the following: the validity of geometric based EE algorithms, the violation of independent restriction in ICA and the ambiguities of ICA. And it proposes a group of novel flexible algorithms by making different strategies for one step EE. Moreover, considering the multi-scale effect of hyperspectral remote sensing data, this dissertation engages in the adaptability research of spectral unmixing techniques based on different spatial scale.
The topics addressed in this dissertation and its main contributions are summarized as follows:
1. Gives a survey for most recent linear spectral unmixing techniques and extends the traditional workflow of end-to-end stages to one that includes state of the art techniques of linear spectral unmixing which makes it more distinct from ordinary ones.
2. Discusses the geometry-based EE techniques from the viewpoint of null space. Two advances based on the null space are presented. First, the subspace projection distance can be easily obtained by null space and a maximal null space projection distance EE algorithm is developed. It is mathematically proven right depended on the relationship between null space and endmember and provides a mathematical basic for the maximal subspace projection distance EE algorithms. Second, null space spectral projection has the invariability of convex. In the light of this characteristic of null space, faster and more flexible method is proposed, called null space spectral projection method. Through making strategies for one step EE, a group of algorithms are designed which present their inimitable virtue in this dissertation.
3. On the issue of ICA for spectral unmixing, component independent restriction is violated due to the abundance constrains. This dissertation presents a solution, called correlation minimizing, to this embarrassment. An optimum angle is provided under the condition of correlation minimizing. Two kinds of modified ICA algorithms, called Sequence oblique-ICA Algorithm (Sob-ICA) and Parallel oblique-ICA Algorithm (Pob-ICA), are presented to adjust the searching direction to the optimum angle for the components. Futhermore, to against the uncertainty of ICA, this dissertation builds up two quantificators to correct the endmember matrix and the abundance respectively and uses an estimated endmembers obtained by EE techniques, such as null space spectral projection algorithm, for the initialized endmember matrix. A workflow of quantified ICA analysis in hyperspectral imagery is presented and it improves the accuracy of spectral unmixing.
Finally, this dissertation presents a technique for evaluating the linear spectral unmixing algorithms, considering the multi-scale effect of hyperspectral remote sensing data. A research on the adaptability of linear spectral unmixing algorithms to different levels of spatial resolution is carried out. The result can provide technical support for the ground spectroradiometer based spectral unmixing research and produce profound thoughts for imporving the spectral unmixing techniques.
“十五”期间，在国家“863”计划和中科院创新项目的支持下，中国科学院遥感应用研究所和上海技术物理所于2006年联合研制成功了多模态航空数字相机系统（Multi-mode Airborne Digital Camera System, MADC）。MADC系统在无人机、直升飞机、中低空以及高空飞机等多种平台上进行了二十余架次的航空数字成像飞行实验，获取了大量高分辨率、高质量的图像数据。鉴于航空飞行平台的不稳定性以及MADC系统设计结构方面的考虑，CCD数字相机的高精度几何成像研究始终是该系统发展的一个重要技术环节。
Multi-mode Airborne Digital Camera System (MADC) was developed by Institute of Remote Sensing Applications and Shanghai Institute of Technical Physics in 2006, under the support by State 863 Projects and Innovation Projects of Chinese Academy of Sciences. MADC system has already obtained many high-resolution and good quality images through more than 20 times aerial photography experiments on different flight platforms, such as unmanned aerial vehicle, helicopter and fixed-wing aircraft. Considering the instability of flight platforms and the special structuredesign of MADC system, we always hold that the high-accurate geometric imagery is an important technique for CCD digital cameras.
In order to improve the geometric accuracy of imaging by MADC system, some important problems are deeply studied in this paper:
1. The significance of developing aerial remote sensing and airborne digital camera system in China was Analyzed; the constructive mode and the principle of geometric imagery according to some airborne digital camera systems at present was researched and summarized; the composition, working principle and aviation experiment of MADC system were introduced; the four key factors which mostly affecting the geometric imagery accuracy of the MADC system were proposed and analyzed.
2. The main cause of the machining precisionreducing and the machining error producing and its common solution method were summarized, the significance of improving machining precisionusing the numerical control technology was analyzed. After the Analyzing of the effect of geometric imagery useing the old type installing base, a new type installing base for MADC system was researched and produced, which can match the machining and installing precisionto the optical devicein a higher level.
3. The effect on the geometric imagery precision which caused by mechanical shutter structure, shutter delay and unsynchronizedexposure were studied. After the introduction of the principle and function of shutter-control system for MADC system, a simple and feasible testing plan in laboratory for measuring the accuracy time of shutter delay and unsynchronizedexposure was proposed. the calculation methodabout this two values was put forwand based on the testing plan. The camera shutter delay and the unsynchronizedexposure of MADC system were determined through the test. The camera shutter delay is up to several tens milliseconds and the unsynchronizedup to millisecond.
4. Vibration has important effect on both the geometric imagery precision and the image quality.The art of the researching of airborne stability platform was sand studied. Based on the deep understanding and nanlyzing of the principle of the aerial stability platform, a feasiable special design of the platform were put forword. The feasibility is supported by detailed calculation and the design is under-making.
5.After the analyzeing of the effect on imaging quality of aerial camerawhich caused by image smear especially the forward image smear, some common methods for image smear compensation were studied. According to the special structure of MADC system, the external image smear compensation module was desighed which can push the whole camera system moving to compensate the image smear and improve the geometric imagery accuracy.
After emphatically analyzing and researching for the key technologieswhich affect the geometric imagery accuracy of MADC system, the hardware and the software of MADC system should be integrated with optimization. The geometric imagery accuracy of MADC system should be greatly improved, which could help us obtainning more high-quality aerial images. This paper can accelerate the improving of the MADC system and get the system become a developed aerial digital photogrammetry system with independent intellectual property of our country.
Hyperspectral imaging is a new and growing technology with the development of airborne and spaceborne remote sensing from the 80s’ of 20th Century. Each pixel in the hyperspectral image is an observation vector and it represents a reflectance spectrum of the materials in the ground area in the Instantaneous Field of View. The Target Detection on hyperspectral imagery is a technique by which the information is obtained based on the transform of spectral dimensions. It can help the expert in many kinds of fields to find the target in the image on the one hand. On the one hand it can replace the Expert Decision to finish the target detection tasks by the method of Artificial Intelligence.
It is a flexible remote sensing procedure to detect the specified targets through hyperspectral images. This dissertation first introduces each detail of this procedure to validate the feasibility of target detection by hyperspectral remote sensing. Many kinds of decorrelation transforms and subspace projection transforms both in Spectral Dimensions are studied for the purpose to understand the hyperspectral analysis in Spectral Dimensions well. And then based on the research of scholars in many fields, this dissertation introduces the contents about mathematical models, design procedures, category and performance estimations of different detectors. Then pointing at two different mathematical models, the probability statistics model and the subspace model, the discussions, designs and experiments to target detection algorithms are opened out.
The main fruits of this dissertation are as follows:
1. To affirm the equivalence of the MNF and NAPC transform, they were discussed as two different mathematical transform methods and finally this conclusion is proved. Then it is proved that how the sample classes are scattered have great influences on classification accuracy when using MNF transform. An improved MNF method, in which the Noise Covariance Matrix estimation method is optimized, is introduced for this problem.
2. For the purpose that to estimate the performance of different hyperspectral target detectors, this article sum up the detector performance parameters of different fields, such as the Probability of False Alarm and the Probability of Detection, the character of Constant False-Alarm-Rate, Signal to Interference-plus-Noise Ratio ROC curve and so on. And many detectors are estimated in accordance with these performance parameters through experiments.
3. The detector algorithms based on the probability statistics model are uniformly described in the whitened space. Pointing at how to select a proper decision sufface between targets and backgrounds in the whitened space, the hypothesis that the background submits to normal distribution is denied in this dissertation, and two algorithms based on Elliptically Contoured Distribution (ECD) function are introduced. They are named ECD Detector with Hyperbola Threshold and ECD Detector with Parabola Threshold. They both perform better than ACE detector on Dection Probability when testified by expermiments
4. The detector algorithms based on the subspace model are uniformly described in the Euclidean space in which some target detector are proved equivalent. And a new Generalized Likelihood-Ratio algorithm based on Oblique Subspace Projection is presented. Testified by expermiments, this detector performs well than GLRT detector when the vectors of the background signature are not enough to describe background subspace.
We try to use the MNF transform in the CEM detector and OSP detector. And find that the Eigenvector of MNF can describe the background subspace accurately, but not the image subspace. So MNF can be used in the detectors which are based on background subspace projection. A new Unsupervised OSP algorithm based on MNF transform is presented.
With the deterioration of inland water pollution, monitoring inland water quality is becoming urgent. Monitoring water quality by remote sensing technology has the advantages of rapidness, wide coverage, low cost, and dynamic monitoring over a long period of time. However, monitoring inland water quality by remote sensing is far behind ocean color remote sensing in both development of remote sensors and monitoring approaches. The multi-spectral remote sensing data, which are often used to monitor inland water quality, can not catch the complicated and changeful spectral characteristics of inland waters accurately. Therefore, the accuracy of water quality monitoring from multi-spectral remote sensing data is quite limited. The development of hyperspectral remote sensing technique has provided much opportunity in monitoring inland water quality. Meanwhile, it has also brought challenge to traditional approaches of monitoring inland water quality. Empirical and semi-empirical approaches, which are often used in monitoring inland water quality, have low robustness, and are hard to be applied to different seasons and areas. In contrast, analytical approaches are based on bio-optical model, and have the advantages of definite physical meanings, higher robustness, and wider applicability. Therefore, it is of great significance to carry out the study on retrieval of inland water quality parameters from hyperspectral remote sensing data by analytical approach.
Taihu Lake is selected as study area in this dissertation. Based on the experiment data acquired in Taihu Lake in four seasons, five aspects of researches are accomplished: 1) measure inherent optical properties and analyze their temporal and spatial distributing rules; 2) measure apparent optical properties and analyze their spectral characteristics; 3) build bio-optical model and retrieve inherent optical properties; 4) set up and validate analytical approaches to retrieve water qualtiy parameters; 5) retrieve water qualtiy parameters from hyperspectral remote sensing image.
Main contributions of this dissertation can be concluded as follows:
1. Temporal and spatial distributing rules of inherent optical properties and specific inherent optical properties are analyzed for Taihu Lake, and on this basis, a specific inherent optical properties database is build up;
2. Four kinds of spectral indices are defined to classify water grass and algal bloom;
3. A nonlinear optimized method based on bio-optical model is proposed to calculate backscattering coefficient of suspended matter;
4. Based on the theory of matrix inversion approach, nonlinear optimized approach and algebra approach are set up, which take specific inherent optical properties as input parameters;
5. The three kinds of approaches, which are matrix inversion approach, nonlinear optimized approach and algebra approach, are tested by the four times of experiment data. The results show that retrieval accuracy of chloroyphyll-a and suspended matter concentrations are fairly good. For different seasons, the approaches to get best retrieval accuaracy of chloroyphyll-a and suspended matter are commonly not the same, and the bands combinations used in the approaches are also not the same;
6. A flow chart of retrieving water quality parameters from hyperspectral remote sensing images is designed, and a approach to calculate remote sensing reflectance is set up based on atmospheric radiant transfer model 6S. One CHRIS image is employed to test the analytical approach propsed in this dissertation, and the results are quite reasonable.
All the contributions of this dissertation have provided theoretical and methodological support in monitoring inland water quality from hyperspectral remote sensing data.
Design and Implement application software platform is an important feature of thesystem project of the Earth Observing Plan. In this thesis, we focus our research on Hyperspectral Remote Sensing Environment Monitoring System and its applications to water resources. To meet the requirements for water quality monitoring in China, a Remote-sensing Environmental Monitoring System (REMS) is introduced. REMS isthe first integrated system in developed for multi-resource, multi-temporal, and multi-thematic data processing and data analysis, and for distributing products for the monitoring of inland water pollution using remote sensing technology. REMS provides the ability to quickly extract the major characteristics of water resources, such as chlorophyll content, total suspended matter (TSM), yellow substance, and blue algae distribution. In order to improve the precision of water parameter extraction, new algorithms and functions are also developed and integrated into the REMS software platform. We select the Taihu Lake in Jiangsu Province, China, as the research area. Finally, we discuss some field applications constructed based on REMS.
My dissertation includes the following:
(1) The introduction of hyper-spectral data algebra structure to hyperspectral algebra structure analysis (HASA).
(2) The establishment of semi-empirical and analytical models to extract the major characteristics of water resources such as chlorophyll content, total suspended matter (TSM), Yellow Substance, and CDOM. The analytical models are based on bio-optical models and radiation-transfer theory, where the optical properties and water quality parameters have distinct physical meaning and universal applicability.
(3) The introduction of fractal-based image compression algorithms using wavelet transformation for hyperspectral images in Chapter four. This algorithm is superior to other traditional compression methods because it has high compression ratios, good image fidelity, and requires less computation time. I present a fast fractal image coding methodology based on wavelet decomposition. The subimage blocks are improved by Pyramidal Haar transform. Furthermore, the improvement of the Quadtree partition is also discussed. My simulations show that the coding time is 100 times faster when the same coding result is retained. The HV and Quadtree partitioning and the domain-range matching algorithms have also been improved to accelerate the encode/decode efficiency.
(4) The design of the most important components of the Hyperspectral Remote Sensing Image Processing and Analysis System, including tools for input/output, preprocessing, data visualization, information extraction, conventional image analysis, advanced tools, and integrated interface to connect with general spectral databases. The base architecture was specially designed and implemented to meet the requirements for the rapid preprocessing of imaging spectrometer data and the easy prototyping of algorithms. Some new methodologies for data analysis and processing were developed and applied to obtain results based on the architecture including mineral identification, agriculture investigation, and urban mapping. The key technologies of the Hyperspectral Remote Sensing Image Processing and Analysis System include the following: image tiling and processing, execution architecture, general data preprocessing, hyperspectral data calibration, hyperspectral data analysis, and image classification.
(5) The design of a new architecture for REMS. REMS provides the ability to extract the major characteristics of water resources, such as chlorophyll content, total suspended matter (TSM), and yellow substance. The most important components of REMS are presented in chapter Six, including tools for multi-resource data input/output, preprocessing, data visualization and mapping, environment information extraction, conventional image analysis, advanced tools for eco-environment modeling, and integrated interface to connect with general spatial and spectral database and water quality monitoring database. We established a REMS internet information publishing system REMS IS. Integrated with WebGIS and RS technology, REMS IS has four tiers and five subsystems, so that it can meet the specification of the B/S and C/S architecture. The four-tier architecture includes the following: the REMS application server, the middle data server, the web server, and the client. The REMS application server is composed with two important modules: the atmosphere correcting module and the water quality parameter inversion module 。。。。。。。。。...
Hyperspectral remote sensing images incorporate properties of geometry, spectra and radiance. These features make facilitate detection and recognition of targets, and largely promote the development of target detection using hyperspectral imagery. Hyperspectral remote sensing provides detailed spectral data for target detection and recognition, the data provides our subject opportunity. However, due to detectors’ capability limitations, hyperspectral images always do not have high spatial response. Therefore, interested targets are exposed with low probability commonly. Traditional target detection methods based on spatial features are invalid for hyperspectral images. How to enhance the strong points and how to use this information is an important issue for target detection in hyperspectral imagery. Thereby it is quite necessary to make comprehensive researches on spectra enhancement and feature extraction.
Based on integrated study of the actuality of target detection using hyperspectral imagery, this dissertation analyzes four key problems in the subject, namley spectral uncertainty, dimension reduction and image denoising, mixed pixel and endmember extraction, low probability exposed target detection. The research is focused on noise estimate, data dimension reduction and feature extraction, along with that, analyzes influences of spectral uncertainty, remote sensing system and data pretreatment on target detection are also discussed. At last, more robust detection algorithms for low low probability exposed target are developed. The main aspects of this dissertation are as follows:
1. The general noise model in imaging spectrometer is discussed. To reduce the pressure of image content to traditional local means and local standard deviations method, three improved methods are developed, which are based on edges eliminate, Gaussian curve extraction and residual adjustment. At the same time, a new noise estimate method based on homogeneous region division is developed. This method is more reliable and adaptable, and works well for hyperspectral images with diverse land cover types.
2. This dissertation study widely used dimension reduction methods in hyperspectral imagery. Some key questions in maximum noise fraction are also analyzed. Based on analysis of characters of these dimension reduction methods, such as sensitivity to magnitude change and noise, information loss and data structure change, a dimension reduction method selection strategy for target detection is developed.
3. Spectral uncertainty due to human activities is studied. Results show that man-made camouflage change the spectra of the target in the full band ranges, but spectral characteristics in short-wave infrared range can be used to distinguish different materials under paints. Paints can only affect the amplitude of spectra, and the shapes of the spectra are fairly consistent. Therefore, spectral shape should be the main measurement to detect targets.
4. Hyperspectral imagery measurements and data processing are studied. Six aspects contain paints, spectral response, imaging spectrometer mode, image noise and dimension reduction are discussed. The influences of these factors to target detection are analyzed. This research provides a strategy to precision control of target detection in hyperspectral imagery.
5. The form of low probability exposed target existed in hyperspectral imagery are studied. The phenomenon of mixed pixel in hyperspectral target detection is also discussed. A low noise sensitive and partial restricted independent component analysis method is developed for decomposition of mixed pixels. Based on the idea of multiple algorithm fusion, spatial continuity, convex volume, independent component analysis and RX detector are combined together to extract endmember quickly and to detect target accurately.
Nowadays, with the development of the aerial photogrammetry, airborne digital camera system played more and more important role in the surveying and remote sensing.Meanwhile, optical sensor of photogrammetry keeping development in the direction of digital、wide array CCD、combining POS system and multi-digital cameras. So that, the new optical sensor puts many new problems and challenges forward the process of photogrammetry data. In the cameras calibration and image process, single digital camera system has been formed relatively perfect processing flow, with a lot of references and technical standards. On the contracy, using traditional method of calibtation and image process brings many problems in the multi-digital cameras system because of the relatively complicated process flow.
In order to deduce the algithmn of calibration method and automatic mosaic of the airborne digital camera system, some important problems are deeply studied in this paper:
1. The significance of developing aerial remote sensing and airborne digital camera system in China was Analyzed; the constructive mode and the principle of geometric imagery according to some airborne digital camera systems at present was researched and summarized; the composition, working principle and aviation experiment of multi-digital cameras system were introduced; the importance of calibration in the airborne digital camera system and the problems of calibration and automatic mosaic was analayzed and summarized.
2. The reason of image motion and basic requirements of compensation was analyzed, and a new external backstepping type image motion compensation device based on the micro actuators was designed. By the experiment, the new device can effectively compensate image motion. Then, a great problem caused by backstepping type image motion compensation was analyzed. The problem is that the inner orientation elements of camera were changed in the moment of compensation. Therefore, the algrithomn was deduced in order to quantative analyzed the effect sizes to the precise of post-processing because of the changes of inner orientation elements and some solutions were given.
3. The difference of camera calibration between multi-digital cameras system and single digital camera system was researched and the process of calibration in the multi-digital cameras system was designed;indoor and outdoor 3D control field was build, the concept of System's Interior Orientation Parameters (SIOP) was put forward. Finally, the calibration method of interior orientation parameters、SIOP、distortion model、the correct parameters of exterior orientation parameters were researched.
4. A new multi-digital cameras system based on the concept of tilt-shift was detail analyzed, the Prototype Verification System was build and the estimation of this system was done in the indoor control field. In the calibration experiment, the difference between new system and traditional multi-digital systems wan analyzed and many problems brought by tilt-shift to calibration was solved in order to form new calibration process for the new systems.Finally, by the flight experiment, the paper improved that the new system can avoid many defect of traditional multi-digital cameras system, and can effective express the advantage of multi-digital cameras system.
5. The key technology of automatic mosaic among single group images of multi-digital cameras system and the SIFT algorithnm、image transformation based on the SIOP was researched. Meanwhile, on the basis of improving RANSAC, a modified method of eliminating error match points and optimizing transformation model for distorted images mosaic was researched. Finally, Through studying linear correction method of RRN, the paper put forward a new and high automatic RRN method based on Scale-invariant feature transform(SIFT)
After emphatically analyzing and researching for the key technologiesof calibration and automatic mosaic of Airborne Digital Camera System, the emphatically research of image motion compensation and the process of calibration for multi-digital cameras system was build;The analyse and calibration process of multi-digital cameras system based on the concept of tilt-shif was improved; Automatic mosaic algrithnm among the single group images of multi-digital cameras system was improved. This paper can accelerate the improving of airborne digital camera system, especially multi-digital cameras system in the photography and remote sensing field.
Recently, the atmospheric pollution and awful air have threaten our nation, therefore it cries for the study the atmospheric properties. As a new technology, remote sensing has a lot of advantages.
The paper selected Lake Taihu as our study area. By collecting the spectrum of the water surface, measuring of atmosphere using the sunphotometer, and acquiring the image of the MODIS on the same day, the data were used to get properties of atmosphere. The key point of the study is related to the atmosphere water vapor amount and the aerosol optical depth (AOD). The research results in this paper are presented as following:
1) Propose an improved algorithm for the retrieval of aerosol optical depth using the direct sunlight data of sunphotometer. Different effects on the inversion errors of AOD are mainly analyzed and improved algorithms have been used to get high inversion precision of aerosol optical depth, respectively in the air mass, Rayleigh optical depth, and ozone absorption coefficient etc.Especially in the computation of standard Rayleigh optical depth using the integral form, not only the effects of complex refractive index but also the depolarization ratio. The computed results have been showed that this algorithm can improve the accuracy of the retrieval.
2) A new calibrated method is presented .The different techniques of calibration for the channels of the direct sunlight are compared and applied in the actual measurements. Except of the Langley method and the standard translated one, a new calibrated one is presented in the theory.
3) As the atmosphere water vapor amount is inverted using the sunphotometer and MODIS data. The good effects are shown in the instant method of the Langley and the ratio of reflectance for two bands and the water vapor amount of every image pixel are obtained.
4) A method based on measurements for determining the aerosol type is announced. By using the nearly synchronous measurements of the data from surface spectrum and the sunphotometer with the image, and by use of the radiative transfer model 6S, varying the components of the aerosol type, a LUT (look up table) is made for the radiance on the satellite. When the total relative error of the new defined parameter for relative error is getting to the least, the aerosol type will be decided.
5)The Mie code which was used to calculate the single sphere particle has been improved, extended and realized to compute the optical characteristics including polarization of the poly –disperse aerosols. And it has been used to get the polarized phase function of aerosols over Lake Taihu.
The results of this study will be provided for the supporting of the atmospheric corrections and the water color remote sensing.