InhaltsangabePREFACE xxiii 1 OVERVIEWAND INTRODUCTION 1 I: PRELIMINARIES 31 2 FUNDAMENTALS OF SUBSAMPLE AND MIXED SAMPLE ANALYSES 33 3 THREEDIMENSIONAL RECEIVER OPERATING CHARACTERISTICS (3D ROC) ANALYSIS 63 4 DESIGN OF SYNTHETIC IMAGE EXPERIMENTS 101 5 VIRTUAL DIMENSIONALITY OF HYPERSPECTRAL DATA 124 6 DATA DIMENSIONALITY REDUCTION 168 II: ENDMEMBER EXTRACTION 201 7 SIMULTANEOUS ENDMEMBER EXTRACTION ALGORITHMS (SM-EEAs) 207 8 SEQUENTIAL ENDMEMBER EXTRACTION ALGORITHMS (SQ-EEAs) 241 (UNCLSEEA) 249 9 INITIALIZATION-DRIVEN ENDMEMBER EXTRACTION ALGORITHMS (ID-EEAs) 265 10 RANDOM ENDMEMBER EXTRACTION ALGORITHMS (REEAs) 287 11 EXPLORATION ON RELATIONSHIPS AMONG ENDMEMBER EXTRACTION ALGORITHMS 316 III: SUPERVISED LINEAR HYPERSPECTRAL MIXTURE ANALYSIS 351 12 ORTHOGONAL SUBSPACE PROJECTION REVISITED 355 13 FISHER'S LINEAR SPECTRAL MIXTURE ANALYSIS 391 14 WEIGHTED ABUNDANCE-CONSTRAINED LINEAR SPECTRAL MIXTURE ANALYSIS 411 15 KERNELBASED LINEAR SPECTRAL MIXTURE ANALYSIS 434 IV: UNSUPERVISED HYPERSPECTRAL IMAGE ANALYSIS 465 16 HYPERSPECTRAL MEASURES 469 17 UNSUPERVISED LINEAR HYPERSPECTRAL MIXTURE ANALYSIS 483 18 PIXEL EXTRACTION AND INFORMATION 526 V: HYPERSPECTRAL INFORMATION COMPRESSION 541 19 EXPLOITATION-BASED HYPERSPECTRAL DATA COMPRESSION 545 20 PROGRESSIVE SPECTRAL DIMENSIONALITY PROCESS 581 21 PROGRESSIVE BAND DIMENSIONALITY PROCESS 613 22 DYNAMIC DIMENSIONALITYALLOCATION 664 23 PROGRESSIVE BAND SELECTION 683 VI: HYPERSPECTRAL SIGNAL CODING 717 24 BINARY CODING FOR SPECTRAL SIGNATURES 719 25 VECTOR CODING FOR HYPERSPECTRAL SIGNATURES 741 26 PROGRESSIVE CODING FOR SPECTRAL SIGNATURES 772 VII: HYPERSPECTRAL SIGNAL CHARACTERIZATION 797 27 VARIABLENUMBERVARIABLEBAND SELECTION FOR HYPERSPECTRAL SIGNALS 799 28 KALMAN FILTER-BASED ESTIMATION FOR HYPERSPECTRAL SIGNALS 820 29 WAVELET REPRESENTATION FOR HYPERSPECTRAL SIGNALS 859 VIII: APPLICATIONS 877 30 APPLICATIONS OF TARGET DETECTION 879 31 NONLINEAR DIMENSIONALITY EXPANSION TO MULTISPECTRAL IMAGERY 897 32 MULTISPECTRAL MAGNETIC RESONANCE IMAGING 920 33 CONCLUSIONS 956 GLOSSARY 993 APPENDIX: ALGORITHM COMPENDIUM 997 REFERENCES 1052 INDEX 1071

InhaltsangabePREFACE xxiii 1 OVERVIEWAND INTRODUCTION 1 1.1 Overview 2 1.2 Issues of Multispectral and Hyperspectral Imageries 3 1.3 Divergence of Hyperspectral Imagery from Multispectral Imagery 4 1.4 Scope of This Book 7 1.5 Book's Organization 10 1.6 Laboratory Data to be Used in This Book 19 1.7 Real Hyperspectral Images to be Used in this Book 20 1.8 Notations and Terminologies to be Used in this Book 29 I: PRELIMINARIES 31 2 FUNDAMENTALS OF SUBSAMPLE AND MIXED SAMPLE ANALYSES 33 2.1 Introduction 33 2.2 Subsample Analysis 35 2.3 Mixed Sample Analysis 45 2.4 KernelBased Classification 57 2.5 Conclusions 60 3 THREEDIMENSIONAL RECEIVER OPERATING CHARACTERISTICS (3D ROC) ANALYSIS 63 3.1 Introduction 63 3.2 NeymanPearson Detection Problem Formulation 65 3.3 ROC Analysis 67 3.4 3D ROC Analysis 69 3.5 Real Data-Based ROC Analysis 72 3.6 Examples 78 3.7 Conclusions 99 4 DESIGN OF SYNTHETIC IMAGE EXPERIMENTS 101 4.1 Introduction 102 4.2 Simulation of Targets of Interest 103 4.3 Six Scenarios of Synthetic Images 104 4.4 Applications 112 4.5 Conclusions 123 5 VIRTUAL DIMENSIONALITY OF HYPERSPECTRAL DATA 124 5.1 Introduction 124 5.2 Reinterpretation of VD 126 5.3 VD Determined by Data Characterization-Driven Criteria 126 5.4 VD Determined by Data Representation-Driven Criteria 140 5.5 Synthetic Image Experiments 144 5.6 VD Estimated for Real Hyperspectral Images 155 5.7 Conclusions 163 6 DATA DIMENSIONALITY REDUCTION 168 6.1 Introduction 168 6.2 Dimensionality Reduction by Second-Order Statistics-Based Component Analysis Transforms 170 6.3 Dimensionality Reduction by High-Order Statistics-Based Components Analysis Transforms 179 6.4 Dimensionality Reduction by Infinite-Order Statistics-Based Components Analysis Transforms 184 6.5 Dimensionality Reduction by Projection Pursuit-Based Components Analysis Transforms 190 6.6 Dimensionality Reduction by Feature Extraction-Based Transforms 195 6.7 Dimensionality Reduction by Band Selection 196 6.8 Constrained Band Selection 197 6.9 Conclusions 198 II: ENDMEMBER EXTRACTION 201 7 SIMULTANEOUS ENDMEMBER EXTRACTION ALGORITHMS (SM-EEAs) 207 7.1 Introduction 208 7.2 Convex Geometry-Based Endmember Extraction 209 7.3 SecondOrder StatisticsBased Endmember Extraction 228 7.4 Automated Morphological Endmember Extraction (AMEE) 230 7.5 Experiments 231 7.6 Conclusions 239 8 SEQUENTIAL ENDMEMBER EXTRACTION ALGORITHMS (SQ-EEAs) 241 8.1 Introduction 241 8.2 Successive N-FINDR (SC N-FINDR) 244 8.3 Simplex Growing Algorithm (SGA) 244 8.4 Vertex Component Analysis (VCA) 247 8.5 Linear Spectral Mixture Analysis-Based SQ-EEAs 248 8.6 HighOrder StatisticsBased SQEEAS 252 8.7 Experiments 254 8.8 Conclusions 262 9 INITIALIZATION-DRIVEN ENDMEMBER EXTRACTION ALGORITHMS (ID-EEAs) 265 9.1 Introduction 265 9.2 Initialization Issues 266 9.3 Initialization-Driven EEAs 271 9.4 Experiments 278 9.5 Conclusions 283 10 RANDOM ENDMEMBER EXTRACTION ALGORITHMS (REEAs) 287 10.1 Introduction 287 10.2 Random PPI (RPPI) 288 10.3 Random VCA (RVCA) 290 10.4 Random N-FINDR (RN-FINDR) 290 10.5 Random SGA (RSGA) 292 10.6 Random ICA-Based EEA (RICA-EEA) 292 10.7 Synthetic Image Experiments 293 10.8 Real Image Experiments 305 10.9 Conclusions 313 11 EXPLORATION ON RELATIONSHIPS AMONG ENDMEMBER EXTRACTION ALGORITHMS 316 11.1 Introduction 316 11.2 Orthogonal Projection-Based EEAs 318 11.3 Comparative Study and Analysis Between SGA and VCA 330 11.4 Does an Endmember Set Really Yield Maximum Simplex Volume? 339 11.5 Impact of Dimensionality Reduction on EEAs 344 11.6 Conclusions 348 III: SUPERVISED LINEAR HYPERSPECTRAL MIXTURE ANALYSIS 351 12 ORTHOGONAL SUBSPACE PROJECTION REVISITED 355 12.1 Introduction 355 12.2 Three Perspectives to Derive OSP 358 12.3 Gaussian Noise in O

Leseprobe