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.
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.
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.
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.