高光谱图像处理技术 英文PDF|Epub|txt|kindle电子书版本网盘下载

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1 Basic Theory and Main Processing Techniques of Hyperspectral Remote Sensing1

1.1 Basic Theory of Hyperspectral Remote Sensing1

1.1.1 Theory of Remote Electromagnetic Wave1

1.1.2 Interaction of Solar Radiation and Materials2

1.1.3 Imaging Spectrometer and Spectral Imaging Modes3

1.1.4 Imaging Characteristics of HSI7

1.2 Classification Technique of HSI8

1.2.1 Supervised Classifications and Unsupervised Classifications8

1.2.2 Parameter Classifications and Nonparameter Classifications11

1.2.3 Crisp Classifications and Fuzzy Classifications13

1.2.4 Other Classification Methods13

1.3 Endmember Extraction Technique of HSI14

1.4 Spectral Unmixing Technique of HSI17

1.4.1 Nonlinear Model18

1.4.2 Linear Model19

1.4.3 Multi-endmember Mode of Linear Model23

1.5 Sub-pixel Mapping Technique of HSI24

1.5.1 Spatial Correlation-Based Sub-pixel Mapping26

1.5.2 Spatial Geostatistics-Based Sub-pixel Mapping28

1.5.3 Neural Network-Based Sub-pixel Mapping29

1.5.4 Pixel-Swapping Strategy-Based Sub-pixel Mapping30

1.6 Super Resolution Technique of HSI32

1.7 Anomaly Detection Technique of HSI35

1.8 Dimensionality Reduction and Compression Technique for HSI38

1.8.1 Dimensionality Reduction：Band Selection and Feature Extraction38

1.8.2 Compression：Lossy Compression and Lossless Compression42

References44

2 Classification Technique for HSI45

2.1 Typical Classification Methods45

2.2 Typical Assessment Criterions48

2.3 SVM-Based Classification Method50

2.3.1 Theory Foundation50

2.3.2 Classification Principle52

2.3.3 Construction of Multi-class Classifier with the Simplest Structure60

2.3.4 Least Squares SVM and Its SMO Optimization Algorithm63

2.3.5 Triply Weighted Classification Method66

2.4 Performance Assessment for SVM-Based Classification70

2.4.1 Performance Assessment for Original SVM-Based Classification72

2.4.2 Performance Assessment for Multi-class Classifier with the Simplest Structure73

2.4.3 Performance Assessment for Triply Weighted Classification74

2.5 Chapter Conclusions76

References77

3 Endmember Extraction Technique of HSI79

3.1 Endmember Extraction Method：N-FINDR79

3.1.1 Introduction of Related Theory79

3.1.2 N-FINDR Algorithm82

3.2 Distance Measure-Based Fast N-FINDR Algorithm84

3.2.1 Substituting Distance Measure for Volume One84

3.2.2 PPI Concept-Based Pixel Indexing86

3.2.3 Complexity Analysis and Efficiency Assessment87

3.3 Linear LSSVM-Based Distance Calculation87

3.4 Robust Method in Endmember Extraction89

3.4.1 In the Pre-processing Stage：Obtaining of Robust Covariance Matrix89

3.4.2 In Endmember Extraction Stage：Deletion of Outliers92

3.5 Performance Assessment92

3.5.1 Distance Measure-Based N-FINDR Fast Algorithm92

3.5.2 Robustness Assessment94

3.6 Two Applications of Fast N-FINDR Algorithm98

3.6.1 Construction of New Solving Algorithm for LSMM98

3.6.2 Construction of Fast and Unsupervised Band Selection Algorithm99

3.7 Chapter Conclusions103

References103

4 Spectral Unmixing Technique of HSI105

4.1 LSMM-Based LSMA Method105

4.2 Two New Solving Methods for Full Constrained LSMA108

4.2.1 Parameter Substitution Method in Iteration Solving Method108

4.2.2 Geometric Solving Method109

4.3 The Principle of LSVM-Based Spectral Unmixing114

4.3.1 Equality Proof of LSVM and LSMM for Spectral Unmixing114

4.3.2 The Unique Superiority of LSVM-Based Unmixing116

4.4 Spatial-Spectral Information-Based Unmixing Method117

4.5 SVM-Based Spectral Unmixing Model with Unmixing Residue Constraints118

4.5.1 Original LSSVM-Based Spectral Unmixing119

4.5.2 Construction of Spectral Unmixing Model Based on Unmixing Residue Constrained LSSVM and Derivation of Its Closed form Solution121

4.5.3 Substituting Multiple Endmembers for Single One in the New Model124

4.6 Performance Assessment125

4.6.1 Performance Assessment for Original SVM-Based Spectral Unmixing125

4.6.2 Assessment on Robust Weighted SVM-Based Unmixing127

4.6.3 Assessment on Spatial-Spectral Unmixing Method129

4.6.4 Performance Assessment on New SVM Unmixing Model with Unmixing Residue Constraints131

4.7 Fuzzy Method of Accuracy Assessment of Spectral Unmixing135

4.7.1 Fuzzy Method of Accuracy Assessment135

4.7.2 Application of Fuzzy Method of Accuracy Assessment in Experiments138

4.8 Chapter Conclusions144

References144

5 Subpixel Mapping Technique of HSI147

5.1 Subpixel Mapping for a Land Class with Linear Features Using a Least Square Support Vector Machine（LSSVM）149

5.1.1 Subpixel Mapping Based on the Least Square Support Vector Machine（LSSVM）150

5.1.2 Artificially Synthesized Training Samples152

5.2 Spatial Attraction-Based Subpixel Mapping（SPSAM）154

5.2.1 Subpixel Mapping Based on the Modified Subpixel/Pixel Spatial Attraction Model（MSPSAM）154

5.2.2 Subpixel Mapping Based on the Mixed Spatial Attraction Model（MSAM）158

5.3 Subpixel Mapping Using Markov Random Field with Subpixel Shifted Remote Sensing Images163

5.3.1 Markov Random Field-Based Subpixel Mapping163

5.3.2 Markov Random Field-Based Subpixel Mapping with Subpixel Shifted Remote-Sensing Images167

5.4 Accuracy Assessment170

5.4.1 Subpixel Mapping for Land Class with Linear Features Using the Least Squares Support Vector Machine（LSSVM）170

5.4.2 MSPSAM and MSAM173

5.4.3 MRF-Based Subpixel Mapping with Subpixel Shifted Remote-Sensing Images178

5.5 Chapter Conclusions183

References184

6 Super-Resolution Technique of HSI187

6.1 POCS Algorithm-Based Super-Resolution Recovery187

6.1.1 Basic Theory of POCS187

6.1.2 POCS Algorithm-Based Super-Resolution Recovery189

6.2 MAP Algorithm-Based Super-Resolution Recovery193

6.2.1 Basic Theory of MAP193

6.2.2 MAP Algorithm-Based Super-Resolution Recovery197

6.3 Resolution Enhancement Method for Single Band199

6.3.1 Construction of Geometric Dual Model and Interpolation Method200

6.3.2 Mixed Interpolation Method203

6.4 Performance Assessment206

6.4.1 POCS and MAP-Based Super-Resolution Methods206

6.4.2 Dual Interpolation Method209

6.5 Chapter Conclusions215

References216

7 Anomaly Detection Technique of HSI217

7.1 Kernel Detection Algorithm Based on the Theory of the Morphology217

7.1.1 Band Selection Based on Morphology218

7.1.2 Kernel RX Algorithm Based on Morphology221

7.2 Adaptive Kernel Anomaly Detection Algorithm224

7.2.1 The Method of Support Vector Data Description225

7.2.2 Adaptive Kernel Anomaly Detection Algorithm228

7.3 Construction of Spectral Similarity Measurement Kemel in Kernel Anomaly Detection232

7.3.1 The Limitations of Gaussian Radial Basis Kernel233

7.3.2 Spectral Similarity Measurement Kernel Function234

7.4 Performance Assessment238

7.4.1 Effect Testing of Morphology-Based Kernel Detection Algorithm238

7.4.2 Effect Testing of Adaptive Kernel Anomaly Detection Algorithm241

7.4.3 Effect Testing of Spectral Similarity Measurement Kernel-Based Anomaly Detection Algorithm244

7.5 Introduction of Other Anomaly Detection Algorithms249

7.5.1 Spatial Filtering-Based Kernel RX Anomaly Detection Algorithm249

7.5.2 Multiple Window Analysis-Based Kernel Detection Algorithm252

7.6 Summary255

References256

8 Dimensionality Reduction and Compression Technique of HSI257

8.1 Dimensionality Reduction Technique257

8.1.1 SVM-Based Band Selection257

8.1.2 Application of Typical Endmember Methods-based Band Selection262

8.1.3 Simulation Experiments264

8.2 Compression Technique266

8.2.1 Vector Quantization-based Compression Algorithm266

8.2.2 Lifting Scheme-based Compression Algorithm273

8.3 Chapter Conclusions279

References280

9 Introduction of Hyperspectral Remote Sensing Applications283

9.1 Agriculture283

9.1.1 Wheat283

9.1.2 Paddy285

9.1.3 Soybean285

9.1.4 Maize286

9.2 Forest286

9.2.1 Forest Investigation286

9.2.2 Forest Biochemical Composition and Forest Health Status289

9.2.3 Forest Disaster290

9.2.4 Exotic Species Monitoring291

9.3 Meadow291

9.3.1 Biomass Estimation in Meadow292

9.3.2 Grassland Species Identification293

9.3.3 Chemical Constituent Estimation294

9.4 Ocean295

9.4.1 Basic Research on Ocean Remote Sensing295

9.4.2 Application Research on Resource and Environment Monitoring of Ocean and Coastal Zone296

9.4.3 International Development Trend297

9.5 Geology298

9.5.1 Mineral Identification299

9.5.2 Resource Exploration300

9.6 Environment304

9.6.1 Atmospheric Pollution Monitoring304

9.6.2 Soil Erosion Monitoring305

9.6.3 Water Environment Monitoring305

9.7 Military Affairs306

References308

Appendix309