Hyperspectral Data Processing : Algorithm Design and Analysis

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Author(s):	Chang, Chein-I
ISBN No.:	9780471690566
Pages:	1,168
Year:	201304
Format:	Trade Cloth (Hard Cover)
Price:	$ 328.37
Dispatch delay:	Dispatched between 7 to 15 days
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PREFACE 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 MultispectralImagery 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 ANALYSES33 2.1 Introduction 33 2.2 Subsample Analysis 35 2.3 Mixed Sample Analysis 45 2.4 Kernel-Based Classification 57 2.5 Conclusions 60 3 THREE-DIMENSIONAL RECEIVER OPERATING CHARACTERISTICS (3DROC) ANALYSIS 63 3.1 Introduction 63 3.2 Neyman-Pearson 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 Criteria126 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-BasedComponent Analysis Transforms 170 6.3 Dimensionality Reduction by High-Order Statistics-BasedComponents Analysis Transforms 179 6.4 Dimensionality Reduction by Infinite-Order Statistics-BasedComponents Analysis Transforms 184 6.5 Dimensionality Reduction by Projection Pursuit-BasedComponents Analysis Transforms 190 6.6 Dimensionality Reduction by Feature Extraction-BasedTransforms 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 Second-Order Statistics-Based 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 High-Order Statistics-Based SQ-EEAS 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 EXTRACTIONALGORITHMS 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 ANALYSIS351 12 ORTHOGONAL SUBSPACE PROJECTION REVISITED 355 12.1 Introduction 355 12.2 Three Perspectives to Derive OSP 358 12.3 Gaussian Noise in OSP 364 12.4 OSP Implemented with Partial Knowledge 372 12.

5 OSP Implemented Without Knowledge 383 12.6 Conclusions 390 13 FISHER''S LINEAR SPECTRAL MIXTURE ANALYSIS391 13.1 Introduction 391 13.2 Feature Vector-Constrained FLSMA (FVC-FLSMA) 392 13.3 Relationship Between FVC-FLSMA and LCMV, TCIMF, and CEM395 13.4 Relationship Between FVC-FLSMA and OSP 396 13.5 Relationship Between FVC-FLSMA and LCDA 396 13.6 Abundance-Constrained Least Squares FLDA (ACLS-FLDA)397 13.

7 Synthetic Image Experiments 398 13.8 Real Image Experiments 402 13.9 Conclusions 409 14 WEIGHTED ABUNDANCE-CONSTRAINED LINEAR SPECTRAL MIXTUREANALYSIS 411 14.1 Introduction 411 14.2 Abundance-Constrained LSMA (AC-LSMA) 413 14.3 Weighted Least-Squares Abundance-Constrained LSMA 413 14.4 Synthetic Image-Based Computer Simulations 419 14.5 Real Image Experiments 426 14.

6 Conclusions 432 15 KERNEL-BASED LINEAR SPECTRAL MIXTURE ANALYSIS 434 15.1 Introduction 434 15.2 Kernel-Based LSMA (KLSMA) 436 15.3 Synthetic Image Experiments 441 15.4 AVIRIS Data Experiments 444 15.5 HYDICE Data Experiments 460 15.6 Conclusions 462 IV: UNSUPERVISED HYPERSPECTRAL IMAGE ANALYSIS 465 16 HYPERSPECTRAL MEASURES 469 16.1 Introduction 469 16.

2 Signature Vector-Based Hyperspectral Measures for TargetDiscrimanition and Identification 470 16.3 Correlation-Weighted Hyperspectral Measures for TargetDiscrimanition and Identification 472 16.4 Experiments 477 16.5 Conclusions 482 17 UNSUPERVISED LINEAR HYPERSPECTRAL MIXTURE ANALYSIS483 17.1 Introduction 483 17.2 Least Squares-Based ULSMA 486 17.3 Component Analysis-Based ULSMA 488 17.4 Synthetic Image Experiments 490 17.

5 Real-Image Experiments 503 17.6 ULSMAVersus Endmember Extraction 517 17.7 Conclusions 524 18 PIXEL EXTRACTION AND INFORMATION 526 18.1 Introduction 526 18.2 Four Types of Pixels 527 18.3 Algorithms Selected to Extract Pixel Information 528 18.4 Pixel Information Analysis via Synthetic Images 528 18.5 Real Image Experiments 534 18.

6 Conclusions 539 V: HYPERSPECTRAL INFORMATION COMPRESSION 541 19 EXPLOITATION-BASED HYPERSPECTRAL DATA COMPRESSION545 19.1 Introduction 545 19.2 Hyperspectral Information Compression Systems 547 19.3 Spectral/Spatial Compression 549 19.4 Progressive Spectral/Spatial Compression 557 19.5 3D Compression 557 19.6 Exploration-Based Applications 559 19.7 Experiments 561 19.

8 Conclusions 580 20 PROGRESSIVE SPECTRAL DIMENSIONALITY PROCESS 581 20.1 Introduction 582 20.2 Dimensionality Prioritization 584 20.3 Representation of Transformed Components for DP 585 20.4 Progressive Spectral Dimensionality Process 589 20.5 Hyperspectral Compression by PSDP 597 20.6 Experiments for PSDP 598 20.7 Conclusions 608 21 PROGRESSIVE BAND DIMENSIONALITY PROCESS 613 21.

1 Introduction 614 21.2 Band Prioritization 615 21.3 Criteria for Band Prioritization 617 21.4 Experiments for BP 624 21.5 Progressive Band Dimensionality Process 651 21.6 Hyperspectral Compresssion by PBDP 653 21.7 Experiments for PBDP 656 21.8 Conclusions 662 22 DYNAMIC DIMENSIONALITYALLOCATION 664 22.

1 Introduction 664 22.2 Dynamic Dimensionality Allocaction 665 22.3 Signature Discriminatory Probabilties 667 22.4 Coding Techniques for Determining DDA 667 22.5 Experiments for Dynamic Dimensionality Allocation 669 22.6 Conclusions 682 23 PROGRESSIVE BAND SELECTION 683 23.1 Introduction 683 23.2 Band De-Corrleation 684 23.

3 Progressive Band Selection 686 23.4 Experiments for Progressive Band Selection 688 23.5 Endmember Extraction 688 23.6 Land Cover/Use Classification 690 23.7 Linear Spectral Mixture Analysis 694 23.8 Conclusions 715 VI: HYPERSPECTRAL SIGNAL CODING 717 24 BINARY CODING FOR SPECTRAL SIGNATURES 719 24.1 Introduction 719 24.2 Binary Coding 720 24.

3 Spectral Feature-Based Coding 723 24.4 Experiments 725 24.5 Conclusions 740 25 VECTOR CODING FOR HYPERSPECTRAL SIGNATURES 741 25.1 Introduction 741 25.2 Spectral Derivative Feature Coding 743 25.3 Spectral Feature Probabilistic Coding 755 25.4 Real Image Experiments 764 25.5 Conclusions 771 26 PROGRESSIVE CODING FOR SPECTRAL SIGNATURES 772 26.

1 Introduction 772 26.2 Multistage Pulse Code Modulation 774 26.3 MPCM-Based Progressive Spectral Signature Coding 783 26.4 NIST-GAS Data Experiments 786 26.5 Real Image Hyperspectral Experiments 790 26.6 Conclusions 796 VII: HYPERSPECTRAL SIGNAL CHARACTERIZATION 797 27 VARIABLE-NUMBERVARIABLE-BAND SELECTION FOR HYPERSPECTRALSIGNALS 799 27.1 Introduction 799 27.2 Orthogonal Subspace Projection-Based Band PrioritizationCriterion 801 27.

3 Variable-Number Variable-Band Selection 803 27.4 Experiments 806 27.5 Selection of Reference S.

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