1 Basic Theory and Main Processing Techniques of Hyperspectral Remote Sensing 1．1 Basic Theory of Hyperspectral Remote Sensing 1．1．1 Theory of Remote Electromagnetic Wave 1．1．2 Interaction of Solar Radiation and Materials 1．1．3 Imaging Spectrometer and Spectral Imaging Modes 1．1．4 Imaging Characteristics of HSI 1．2 Classification Technique of HSI 1．2．1 Supervised Classifications and Unsupervised Classifications 1．2．2 Parameter Classifications and Nonparameter Classifications 1．2．3 Crisp Classifications and Fuzzy Classifications 1．2．4 Other Classification Methods 1．3 Endmember Extraction Technique of HSI 1．4 Spectral Unmixing Technique of HSI 1．4．1 Nonlinear Model 1．4．2 Linear Model 1．4．3 Multi-endmember Mode of Linear Model 1．5 Sub-pixel Mapping Technique of HSI 1．5．1 Spatial Correlation-Based Sub-pixel Mapping 1．5．2 Spatial Geostatistics-Based Sub-pixel Mapping 1．5．3 Neural Network-Based Sub-pixel Mapping 1．5．4 Pixel-Swapping Strategy-Based Sub-pixel Mapping 1．6 Super Resolution Technique of HSI 1．7 Anomaly Detection Technique of HSI 1．8 Dimensionality Reduction and Compression Technique for HSI 1．8．1 Dimensionality Reduction： Band Selection and Feature Extraction 1．8．2 Compression： Lossy Compression and Lossless Compression References 2 Classification Technique for HSI 2．1 Typical Classification Methods 2．2 Typical Assessment Criterions 2．3 SVM-Based Classification Method 2．3．1 Theory Foundation 2．3．2 Classification Principle 2．3．3 Construction of Multi-class Classifier with the Simplest Structure 2．3．4 Least Squares SVM and Its SMO Optimization Algorithm 2．3．5 Triply Weighted Classification Method 2．4 Performance Assessment for SVM-Based Classification 2．4．1 Performance Assessment for Original SVM-Based Classification 2．4．2 Performance Assessment for Multi-class Classifier with the Simplest Structure 2．4．3 Performance Assessment for Triply Weighted Classification 2．5 Chapter Conclusions References 3 Endmember Extraction Technique of HSI 3．1 Endmember Extraction Method： N-FINDR 3．1．1 Introduction of Related Theory 3．1．2 N-FINDR Algorithm 3．2 Distance Measure-Based Fast N-FINDR Algorithm 3．2．1 Substituting Distance Measure for Volume One 3．2．2 PPI Concept-Based Pixel Indexing 3．2．3 Complexity Analysis and Efficiency Assessment 3．3 Linear LSSVM-Based Distance Calculation 3．4 Robust Method in Endmember Extraction 3．4．1 In the Pre-processing Stage： Obtaining of Robust Covariance Matrix 3．4．2 In Endmember Extraction Stage： Deletion of Outliers 3．5 Performance Assessment 3．5．1 Distance Measure-Based N-FINDR Fast Algorithm 3．5．2 Robustness Assessment 3．6 Two Applications of Fast N-FINDR Algorithm 3．6．1 Construction of New Solving Algorithm for LSMM 3．6．2 Construction of Fast and Unsupervised Band Selection Algorithm 3．7 Chapter Conclusions References 4 Spectral Unmixing Technique of HSI 4．1 LSMM-Based LSMA Method 4．2 Two New Solving Methods for Full Constrained LSMA 4．2．1 Parameter Substitution Method in Iteration Solving Method 4．2．2 Geometric Solving Method 4．3 The Principle of LSVM-Based Spectral Unmixing 4．3．1 Equality Proof of LSVM and LSMM for Spectral Unmixing 4．3．2 The Unique Superiority of LSVM-Based Unmixing 4．4 Spatial-Spectral Information-Based Unmixing Method 4．5 SVM-Based Spectral Unmixing Model with Unmixing Residue Constraints 4．5．1 Original LSSVM-Based Spectral Unmixing 4．5．2 Construction of Spectral Unmixing Model Based on Unmixing Residue Constrained LSSVM and Derivation of Its Closed form Solution 4．5．3 Substituting Multiple Endmembers for Single One in the New Model 4．6 Performance Assessment 4．6．1 Performance Assessment for Original SVM-Based Spectral Unmixing 4．6．2 Assessment on Robust Weighted SVM-Based Unmixing 4．6．3 Assessment on Spatial-Spectral Unmixing Method 4．6．4 Performance Assessment on New SVM Unmixing Model with Unmixing Residue Constraints 4．7 Fuzzy Method of Accuracy Assessment of Spectral Unmixing 4．7．1 Fuzzy Method of Accuracy Assessment 4．7．2 Application of Fuzzy Method of Accuracy Assessment in Experiments 4．8 Chapter Conclusions References 5 Subpixel Mapping Technique of HSI 5．1 Subpixel Mapping for a Land Class with Linear Features Using a Least Square Support Vector Machine （LSSVM） 5．1．1 Subpixel Mapping Based on the Least Square Support Vector Machine （LSSVM） 5．1．2 Artificially Synthesized Training Samples 5．2 Spatial Attraction-Based Subpixel Mapping （SPSAM） 5．2．1 Subpixel Mapping Based on the Modified Subpixel/Pixel Spatial Attraction Model （MSPSAM） 5．2．2 Subpixel Mapping Based on the Mixed Spatial Attraction Model （MSAM） 5．3 Subpixel Mapping Using Markov Random Field with Subpixel Shifted Remote Sensing Images 5．3．1 Markov Random Field-Based Subpixel Mapping 5．3．2 Markov Random Field-Based Subpixel Mapping with Subpixel Shifted Remote-Sensing Images 5．4 Accuracy Assessment 5．4．1 Subpixel Mapping for Land Class with Linear Features Using the Least Squares Support Vector Machine （LSSVM） 5．4．2 MSPSAM and MSAM 5．4．3 MRF-Based Subpixel Mapping with Subpixel Shifted Remote-Sensing Images 5．5 Chapter Conclusions References 6 Super-Resolution Technique of HSI 6．1 POCS Algorithm-Based Super-Resolution Recovery 6．1．1 Basic Theory of POCS 6．1．2 POCS Algorithm-Based Super-Resolution Recovery 6．2 MAP Algorithm-Based Super-Resolution Recovery 6．2．1 Basic Theory of MAP 6．2．2 MAP Algorithm-Based Super-Resolution Recovery 6．3 Resolution Enhancement Method for Single Band 6．3．1 Construction of Geometric Dual Model and Interpolation Method 6．3．2 Mixed Interpolation Method 6．4 Performance Assessment 6．4．1 POCS and MAP-Based Super-Resolution Methods 6．4．2 Dual Interpolation Method 6．5 Chapter Conclusions References 7 Anomaly Detection Technique of HSI 7．1 Kernel Detection Algorithm Based on the Theory of the Morphology 7．1．1 Band Selection Based on Morphology 7．1．2 Kernel RX Algorithm Based on Morphology 7．2 Adaptive Kernel Anomaly Detection Algorithm 7．2．1 The Method of Support Vector Data Description 7．2．2 Adaptive Kernel Anomaly Detection Algorithm 7．3 Construction of Spectral Similarity Measurement Kernel in Kernel Anomaly Detection 7．3．1 The Limitations of Gaussian Radial Basis Kernel 7．3．2 Spectral Similarity Measurement Kernel Function 7．4 Performance Assessment 7．4．1 Effect Testing of Morphology-Based Kernel Detection Algorithm 7．4．2 Effect Testing of Adaptive Kernel Anomaly Detection Algorithm 7．4．3 Effect Testing of Spectral Similarity Measurement Kernel-Based Anomaly Detection Algorithm 7．5 Introduction of Other Anomaly Detection Algorithms 7．5．1 Spatial Filtering-Based Kernel RX Anomaly Detection Algorithm 7．5．2 Multiple Window Analysis-Based Kernel Detection Algorithm 7．6 Summary References 8 Dimensionafity Reduction and Compression Technique of HSI 8．1 Dimensionality Reduction Technique 8．1．1 SVM-Based Band Selection 8．1．2 Application of Typical Endmember Methods-based Band Selection 8．1．3 Simulation Experiments 8．2 Compression Technique 8．2．1 Vector Quantization-based Compression Algorithm 8．2．2 Lifting Scheme-based Compression Algorithm 8．3 Chapter Conclusions References 9 Introduction of Hyperspectral Remote Sensing Applications 9．1 Agriculture 9．1．1 Wheat 9．1．2 Paddy 9．1．3 Soybean 9．1．4 Maize 9．2 Forest 9．2．1 Forest Investigation 9．2．2 Forest Biochemical Composition and Forest Health Status 9．2．3 Forest Disaster 9．2．4 Exotic Species Monitoring 9．3 Meadow 9．3．1 Biomass Estimation in Meadow 9．3．2 Grassland Species Identification 9．3．3 Chemical Constituent Estimation 9．4 Ocean 9．4．1 Basic Research on Ocean Remote Sensing 9．4．2 Application Research on Rescurce and Environment Monitoring of Ocean and Coastal Zone 9．4．3 International Development Trend 9．5 Geology 9．5．1 Mineral Identification 9．5．2 Resource Exploration 9．6 Environment 9．6．1 Atmospheric Pollution Monitoring 9，6．2 Soil Erosion Monitoring 9．6．3 Water Environment Monitoring 9．7 Military Affairs References Appendix
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