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Hyperspectral Data Processing: Algorithm Design and Analysis

ISBN: 978-0-471-69056-6
1164 pages
April 2013
Hyperspectral Data Processing: Algorithm Design and Analysis (0471690562) cover image

Hyperspectral Data Processing: Algorithm Design and Analysis is a culmination of the research conducted in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. Specifically, it treats hyperspectral image processing and hyperspectral signal processing as separate subjects in two different categories. Most materials covered in this book can be used in conjunction with the author’s first book, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, without much overlap.

Many results in this book are either new or have not been explored, presented, or published in the public domain. These include various aspects of endmember extraction, unsupervised linear spectral mixture analysis, hyperspectral information compression, hyperspectral signal coding and characterization, as well as applications to conceal target detection, multispectral imaging, and magnetic resonance imaging. Hyperspectral Data Processing contains eight major sections:

  • Part I: provides fundamentals of hyperspectral data processing
  • Part II: offers various algorithm designs for endmember extraction
  • Part III: derives theory for supervised linear spectral mixture analysis
  • Part IV: designs unsupervised methods for hyperspectral image analysis
  • Part V: explores new concepts on hyperspectral information compression
  • Parts VI & VII: develops techniques for hyperspectral signal coding and characterization
  • Part VIII: presents applications in multispectral imaging and magnetic resonance imaging

Hyperspectral Data Processing compiles an algorithm compendium with MATLAB codes in an appendix to help readers implement many important algorithms developed in this book and write their own program codes without relying on software packages.

Hyperspectral Data Processing is a valuable reference for those who have been involved with hyperspectral imaging and its techniques, as well those who are new to the subject.

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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 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 Kernel-Based Classification 57

2.5 Conclusions 60

3 THREE-DIMENSIONAL RECEIVER OPERATING CHARACTERISTICS (3D ROC) 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 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 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 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 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 ANALYSIS 391

13.1 Introduction 391

13.2 Feature Vector-Constrained FLSMA (FVC-FLSMA) 392

13.3 Relationship Between FVC-FLSMA and LCMV, TCIMF, and CEM 395

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 MIXTURE ANALYSIS 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 Target Discrimanition and Identification 470

16.3 Correlation-Weighted Hyperspectral Measures for Target Discrimanition and Identification 472

16.4 Experiments 477

16.5 Conclusions 482

17 UNSUPERVISED LINEAR HYPERSPECTRAL MIXTURE ANALYSIS 483

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 COMPRESSION 545

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 HYPERSPECTRAL SIGNALS 799

27.1 Introduction 799

27.2 Orthogonal Subspace Projection-Based Band Prioritization Criterion 801

27.3 Variable-Number Variable-Band Selection 803

27.4 Experiments 806

27.5 Selection of Reference Signatures 819

27.6 Conclusions 819

28 KALMAN FILTER-BASED ESTIMATION FOR HYPERSPECTRAL SIGNALS 820

28.1 Introduction 820

28.2 Kalman Filter-Based Linear Unmixing 822

28.3 Kalman Filter-Based Spectral Characterization Signal-Processing Techniques 824

28.4 Computer Simulations Using AVIRIS Data 831

28.5 Computer Simulations Using NIST-Gas Data 843

28.6 Real Data Experiments 852

28.7 Conclusions 857

29 WAVELET REPRESENTATION FOR HYPERSPECTRAL SIGNALS 859

29.1 Introduction 859

29.2 Wavelet Analysis 860

29.2.1 Multiscale Approximation 860

29.2.2 Scaling Function 861

29.2.3 Wavelet Function 862

29.3 Wavelet-Based Signature Characterization Algorithm 863

29.4 Synthetic Image-Based Computer Simulations 868

29.5 Real Image Experiments 871

29.6 Conclusions 875

VIII: APPLICATIONS 877

30 APPLICATIONS OF TARGET DETECTION 879

30.1 Introduction 879

30.2 Size Estimation of Subpixel Targets 880

30.3 Experiments 881

30.4 Concealed Target Detection 891

30.5 Computer-Aided Detection and Classification Algorithm for Concealed Targets 892

30.6 Experiments for Concealed Target Detection 893

30.7 Conclusions 895

31 NONLINEAR DIMENSIONALITY EXPANSION TO MULTISPECTRAL IMAGERY 897

31.1 Introduction 897

31.2 Band Dimensionality Expansion 899

31.3 Hyperspectral Imaging Techniques Expanded by BDE 902

31.4 Feature Dimensionality Expansion by Nonlinear Kernels 904

31.5 BDE in Conjunction with FDE 909

31.6 Multispectral Image Experiments 909

31.7 Conclusion 918

32 MULTISPECTRAL MAGNETIC RESONANCE IMAGING 920

32.1 Introduction 920

32.2 Linear Spectral Mixture Analysis for MRI 923

32.3 Linear Spectral Random Mixture Analysis for MRI 928

32.4 Kernel-Based Linear Spectral Mixture Analysis 933

32.5 Synthetic MR Brain Image Experiments 933

32.6 Real MR Brain Image Experiments 951

32.7 Conclusions 955

33 CONCLUSIONS 956

33.1 Design Principles for Nonliteral Hyperspectral Imaging Techniques 956

33.2 Endmember Extraction 964

33.3 Linear Spectral Mixture Analysis 970

33.4 Anomaly Detection 974

33.5 Support Vector Machines and Kernel-Based Approaches 977

33.6 Hyperspectral Compression 981

33.7 Hyperspectral Signal Processing 984

33.8 Applications 987

33.9 Further Topics 987

GLOSSARY 993

APPENDIX: ALGORITHM COMPENDIUM 997

REFERENCES 1052

INDEX 1071

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CHEIN-I CHANG, PhD, is a Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County. He established the Remote Sensing Signal and Image Processing Laboratory and conducts research in designing and developing signal processing algorithms for hyperspectral imaging, medical imaging, and documentation analysis. A Fellow of IEEE and SPIE, Dr. Chang has published over 125 refereed journal articles, including more than forty papers in the IEEE Transaction on Geoscience and Remote Sensing. In addition to authoring Hyperspectral Imaging: Techniques for Spectral Detection and Classification, as well as editing two books, Hyperspectral Data Exploitation: Theory and Applications and Recent Advances in Hyperspectral Signal and Imaging Processing and co-editing one book, High Performance Computing in Remote Sensing, he holds five patents and has several pending.

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