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.
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.
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.
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.
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.
“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.