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HYPERSPECTRAL DATAPROCESSING
HYPERSPECTRAL DATAPROCESSINGAlgorithm Design and Analysis
Chein-I Chang
University of Maryland, Baltimore County (UMBC), Maryland, USA
Copyright # 2013 by John Wiley & Sons, Inc. All rights reserved
Published by John Wiley & Sons, Inc., Hoboken, NJ
Published simultaneously in Canada
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Library of Congress Cataloging-in-Publication Data:
Chang, Chein-I.
Hyperspectral data processing : algorithm design and analysis / Chein-I Chang.
p. cm.
Includes bibliographical references and index.
ISBN 978-0-471-69056-6 (hardback)
1. Image processing–Digital techniques. 2. Spectroscopic imaging. 3. Signal processing. I. Chang, Chein-I.
Hyperspectral imaging. II. Title.
TA1637.C4776 2012
621.39’94–dc23
2011043896
Printed in the United States of America
10 9 8 7 6 5 4 3 2 1
This book is dedicated to members of my family, specifically
my mother who provided me with her timeless support
and encouragement during the course of preparing
this book. It is also dedicated to all of my students
who have contributed to this book.
Contents
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
1.3.1 Misconception: Hyperspectral Imaging is a Natural Extension
of Multispectral Imaging 4
1.3.2 Pigeon-Hole Principle: Natural Interpretation of Hyperspectral Imaging 5
1.4 Scope of This Book 7
1.5 Book’s Organization 10
1.5.1 Part I: Preliminaries 10
1.5.2 Part II: Endmember Extraction 12
1.5.3 Part III: Supervised Linear Hyperspectral Mixture Analysis 13
1.5.4 Part IV: Unsupervised Hyperspectral Analysis 13
1.5.5 Part V: Hyperspectral Information Compression 15
1.5.6 Part VI: Hyperspectral Signal Coding 16
1.5.7 Part VII: Hyperspectral Signal Feature Characterization 17
1.5.8 Applications 17
1.5.8.1 Chapter 30: Applications of Target Detection 17
1.5.8.2 Chapter 31: Nonlinear Dimensionality Expansion to Multispectral
Imagery 18
1.5.8.3 Chapter 32: Multispectral Magnetic Resonance Imaging 19
1.6 Laboratory Data to be Used in This Book 19
1.6.1 Laboratory Data 19
1.6.2 Cuprite Data 19
1.6.3 NIST/EPA Gas-Phase Infrared Database 19
1.7 Real Hyperspectral Images to be Used in this Book 20
1.7.1 AVIRIS Data 20
1.7.1.1 Cuprite Data 21
1.7.1.2 Purdue’s Indiana Indian Pine Test Site 25
1.7.2 HYDICE Data 26
1.8 Notations and Terminologies to be Used in this Book 29
vii
I: PRELIMINARIES 31
2 FUNDAMENTALS OF SUBSAMPLE AND MIXED SAMPLE ANALYSES 33
2.1 Introduction 33
2.2 Subsample Analysis 35
2.2.1 Pure-Sample Target Detection 35
2.2.2 Subsample Target Detection 38
2.2.2.1 Adaptive Matched Detector (AMD) 39
2.2.2.2 Adaptive Subspace Detector (ASD) 41
2.2.3 Subsample Target Detection: Constrained Energy Minimization (CEM) 43
2.3 Mixed Sample Analysis 45
2.3.1 Classification with Hard Decisions 45
2.3.1.1 Fisher’s Linear Discriminant Analysis (FLDA) 46
2.3.1.2 Support Vector Machines (SVM) 48
2.3.2 Classification with Soft Decisions 54
2.3.2.1 Orthogonal Subspace Projection (OSP) 54
2.3.2.2 Target-Constrained Interference-Minimized
Filter (TCIMF) 56
2.4 Kernel-Based Classification 57
2.4.1 Kernel Trick Used in Kernel-Based Methods 57
2.4.2 Kernel-Based Fisher’s Linear Discriminant Analysis (KFLDA) 58
2.4.3 Kernel Support Vector Machine (K-SVM) 59
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
3.5.1 How to Generate ROC Curves from Real Data 72
3.5.2 How to Generate Gaussian-Fitted ROC Curves 73
3.5.3 How to Generate 3D ROC Curves 75
3.5.4 How to Generate 3D ROC Curves for Multiple Signal Detection and
Classification 77
3.6 Examples 78
3.6.1 Hyperspectral Imaging 79
3.6.1.1 Hyperspectral Target Detection 79
3.6.1.2 Linear Hyperspectral Mixture Analysis 80
3.6.2 Magnetic Resonance (MR) Breast Imaging 83
3.6.2.1 Breast Tumor Detection 84
3.6.2.2 Brain Tissue Classification 87
3.6.3 Chemical/Biological Agent Detection 91
3.6.4 Biometric Recognition 95
3.7 Conclusions 99
viii Contents
4 DESIGN OF SYNTHETIC IMAGE EXPERIMENTS 101
4.1 Introduction 102
4.2 Simulation of Targets of Interest 103
4.2.1 Simulation of Synthetic Subsample Targets 103
4.2.2 Simulation of Synthetic Mixed-Sample Targets 104
4.3 Six Scenarios of Synthetic Images 104
4.3.1 Panel Simulations 104
4.3.2 Three Scenarios for Target Implantation (TI) 106
4.3.2.1 Scenario TI1 (Clean Panels Implanted into Clean Background) 106
4.3.2.2 Scenario TI2 (Clean Panels Implanted into Noisy Background) 107
4.3.2.3 Scenario TI3 (Gaussian Noise Added to Clean Panels
Implanted into Clean Background) 108
4.3.3 Three Scenarios for Target Embeddedness (TE) 108
4.3.3.1 Scenario TE1 (Clean Panels Embedded in Clean Background) 109
4.3.3.2 Scenario TE2 (Clean Panels Embedded in Noisy Background) 109
4.3.3.3 Scenario TE3 (Gaussian Noise Added to Clean Panels
Embedded in Background) 110
4.4 Applications 112
4.4.1 Endmember Extraction 112
4.4.2 Linear Spectral Mixture Analysis (LSMA) 113
4.4.2.1 Mixed Pixel Classification 114
4.4.2.2 Mixed Pixel Quantification 114
4.4.3 Target Detection 114
4.4.3.1 Subpixel Target Detection 114
4.4.3.2 Anomaly Detection 122
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
5.3.1 Eigenvalue Distribution-Based Criteria 127
5.3.1.1 Thresholding Energy Percentage 127
5.3.1.2 Thresholding Difference between Normalized Correlation
Eigenvalues and Normalized Covariance Eigenvalues 128
5.3.1.3 Finding First Sudden Drop in the Normalized Eigenvalue
Distribution 128
5.3.2 Eigen-Based Component Analysis Criteria 128
5.3.2.1 Singular Value Decomposition (SVD) 128
5.3.2.2 Principal Components Analysis (PCA) 129
5.3.3 Factor Analysis: Malinowski’s Error Theory 129
5.3.4 Information Theoretic Criteria (ITC) 130
5.3.4.1 AIC 131
5.3.4.2 MDL 131
5.3.5 Gershgorin Radius-Based Methods 131
5.3.5.1 Thresholding Gershgorin Radii 134
5.3.5.2 Thresholding Difference Gershgorin Radii between RL�L and KL�L 134
Contents ix
5.3.6 HFC Method 135
5.3.7 Discussions on Data Characterization-Driven Criteria 138
5.4 VD Determined by Data Representation-Driven Criteria 140
5.4.1 Orthogonal Subspace Projection (OSP) 140
5.4.2 Signal Subspace Estimation (SSE) 142
5.4.3 Discussions on OSP and SSE/HySime 143
5.5 Synthetic Image Experiments 144
5.5.1 Data Characterization-Driven Criteria 144
5.5.1.1 Target Implantation (TI) Scenarios 145
5.5.1.2 Target Embeddedness (TE) Scenarios 146
5.5.2 Data Representation-Driven Criteria 149
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
6.2.1 Eigen Component Analysis Transforms 170
6.2.1.1 Principal Components Analysis 170
6.2.1.2 Standardized Principal Components Analysis 172
6.2.1.3 Singular Value Decomposition 174
6.2.2 Signal-to-Noise Ratio-Based Components Analysis Transforms 176
6.2.2.1 Maximum Noise Fraction Transform 176
6.2.2.2 Noise-Adjusted Principal Component Transform 177
6.3 Dimensionality Reduction by High-Order Statistics-Based Components Analysis
Transforms 179
6.3.1 Sphering 179
6.3.2 Third-Order Statistics-Based Skewness 181
6.3.3 Fourth-Order Statistics-Based Kurtosis 182
6.3.4 High-Order Statistics 182
6.3.5 Algorithm for Finding Projection Vectors 183
6.4 Dimensionality Reduction by Infinite-Order Statistics-Based Components Analysis
Transforms 184
6.4.1 Statistics-Prioritized ICA-DR (SPICA-DR) 187
6.4.2 Random ICA-DR 188
6.4.3 Initialization Driven ICA-DR 189
6.5 Dimensionality Reduction by Projection Pursuit-Based Components Analysis
Transforms 190
6.5.1 Projection Index-Based Projection Pursuit 191
6.5.2 Random Projection Index-Based Projection Pursuit 192
6.5.3 Projection Index-Based Prioritized Projection Pursuit 193
6.5.4 Initialization Driven Projection Pursuit 194
6.6 Dimensionality Reduction by Feature Extraction-Based Transforms 195
6.6.1 Fisher’s Linear Discriminant Analysis 195
6.6.2 Orthogonal Subspace Projection 196
6.7 Dimensionality Reduction by Band Selection 196
x Contents
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
7.2.1 Convex Geometry-Based Criterion: Orthogonal Projection 209
7.2.2 Convex Geometry-Based Criterion: Minimal Simplex Volume 214
7.2.2.1 Minimal-Volume Transform (MVT) 214
7.2.2.2 Convex Cone Analysis (CCA) 214
7.2.3 Convex Geometry-Based Criterion: Maximal Simplex Volume 215
7.2.3.1 Simultaneous N-FINDR (SM N-FINDR) 216
7.2.3.2 Iterative N-FINDR (IN-FINDR) 216
7.2.3.3 Various Versions of Implementing IN-FINDR 218
7.2.3.4 Discussions on Various Implementation Versions of IN-FINDR 222
7.2.3.5 Comparative Study Among Various Versions of IN-FINDR 222
7.2.3.6 Alternative SM N-FINDR 223
7.2.4 Convex Geometry-Based Criterion: Linear Spectral Mixture Analysis 225
7.3 Second-Order Statistics-Based Endmember Extraction 228
7.4 Automated Morphological Endmember Extraction (AMEE) 230
7.5 Experiments 231
7.5.1 Synthetic Image Experiments 231
7.5.1.1 Scenario TI1 (Endmembers Implanted in a Clean Background) 232
7.5.1.2 Scenario TI2 (Endmembers Implanted in a Noisy Background) 233
7.5.1.3 Scenario TI3 (Noisy Endmembers Implanted in a Noisy
Background) 234
7.5.1.4 Scenario TE1 (Endmembers Embedded into a Clean
Background) 235
7.5.1.5 Scenario TE2 (Endmembers Embedded into a Noisy
Background) 235
7.5.1.6 Scenario TE3 (Noisy Endmembers Embedded into a Noisy
Background) 236
7.5.2 Cuprite Data 237
7.5.3 HYDICE Data 237
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
8.5.1 Automatic Target Generation Process-EEA (ATGP-EEA) 248
8.5.2 Unsupervised Nonnegativity Constrained Least-Squares-EEA
(UNCLS-EEA) 249
Contents xi
8.5.3 Unsupervised Fully Constrained Least-Squares-EEA (UFCLS-EEA) 250
8.5.4 Iterative Error Analysis-EEA (IEA-EEA) 251
8.6 High-Order Statistics-Based SQ-EEAS 252
8.6.1 Third-Order Statistics-Based SQ-EEA 252
8.6.2 Fourth-Order Statistics-Based SQ-EEA 252
8.6.3 Criterion for kth Moment-Based SQ-EEA 253
8.6.4 Algorithm for Finding Projection Vectors 253
8.6.5 ICA-Based SQ-EEA 254
8.7 Experiments 254
8.7.1 Synthetic Image Experiments 255
8.7.2 Real Hyperspectral Image Experiments 258
8.7.2.1 Cuprite Data 258
8.7.2.2 HYDICE Data 260
8.8 Conclusions 262
9 INITIALIZATION-DRIVEN ENDMEMBER EXTRACTION
ALGORITHMS (ID-EEAs) 265
9.1 Introduction 265
9.2 Initialization Issues 266
9.2.1 Initial Conditions to Terminate an EEA 267
9.2.2 Selection of an Initial Set of Endmembers for an EEA 267
9.2.3 Issues of Random Initial Conditions Demonstrated by Experiments 268
9.2.3.1 HYDICE Experiments 268
9.2.3.2 AVIRIS Experiments 270
9.3 Initialization-Driven EEAs 271
9.3.1 Initial Endmember-Driven EEAs 272
9.3.1.1 Finding Maximum Length of Data Sample Vectors 272
9.3.1.2 Finding Sample Mean of Data Sample Vectors 273
9.3.2 Endmember Initialization Algorithm for SM-EEAs 274
9.3.2.1 SQ-EEAs 274
9.3.2.2 Maxmin-Distance Algorithm 275
9.3.2.3 ISODATA 275
9.3.3 EIA-Driven EEAs 275
9.4 Experiments 278
9.4.1 Synthetic Image Experiments 278
9.4.2 Real Image Experiments 281
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
10.7.1 RPPI 293
xii Contents
10.7.2 Various Random Versions of IN-FINDR 296
10.7.2.1 Scenario TI2 297
10.7.2.2 Scenario TI3 299
10.7.2.3 TE2 301
10.7.2.4 TE3 Scenario 303
10.8 Real Image Experiments 305
10.8.1 HYDICE Image Experiments 305
10.8.1.1 RPPI 306
10.8.1.2 RN-FINDR 306
10.8.2 AVIRIS Image Experiments 309
10.8.2.1 RPPI 309
10.8.2.2 RN-FINDR 310
10.9 Conclusions 313
11 EXPLORATION ON RELATIONSHIPS AMONG ENDMEMBER
EXTRACTION ALGORITHMS 316
11.1 Introduction 316
11.2 Orthogonal Projection-Based EEAs 318
11.2.1 Relationship among PPI, VCA, and ATGP 319
11.2.1.1 Relationship Between PPI and ATGP 319
11.2.1.2 Relationship Between PPI and VCA 320
11.2.1.3 Relationship Between ATGP and VCA 321
11.2.1.4 Discussions 322
11.2.2 Experiments-Based Comparative Study and Analysis 323
11.2.2.1 Synthetic Image Experiment: TI2 323
11.2.2.2 Real Image Experiments 325
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
12.2.1 Signal Detection Perspective Derived from (d,U)-Model
and OSP-Model 359
12.2.2 Fisher’s Linear Discriminant Analysis Perspective from OSP-Model 360
12.2.3 Parameter Estimation Perspective from OSP-Model 362
12.2.4 Relationship Between dLSap ðrÞ and Least-Squares Linear SpectralMixture Analysis 362
12.3 Gaussian Noise in OSP 364
12.3.1 Signal Detector in Gaussian Noise Using OSP-Model 365
12.3.2 Gaussian Maximum Likelihood Classifier Using OSP-Model 366
12.3.3 Gaussian Maximum Likelihood Estimator 367
12.3.4 Examples 367
Contents xiii
12.4 OSP Implemented with Partial Knowledge 372
12.4.1 CEM 373
12.4.1.1 d Is Orthogonal to U (i.e., P?Ud ¼ d) and R ¼ I
(i.e., Spectral Correlation is Whitened) 374
12.4.1.2 An Alternative Approach to Implementing CEM 374
12.4.1.3 CEM Implemented in Conjunction with P?U 375
12.4.1.4 CEM Implemented in Conjunction with P?U in White Noise 376
12.4.2 TCIMF 377
12.4.2.1 D ¼ mp ¼ d with nD ¼ 1 and U ¼ m1m2 � � �mp�1
� �with
nU ¼ p� 1 378
12.4.2.2 D ¼ mp ¼ d with nD ¼ 1 and U ¼ m1 m2 � � �mp�1
� �with
nU ¼ p� 1 and R ¼ I 378
12.4.2.3 D¼ d and U ¼ Ø (i.e., Only the Desired Signature d is Available) 378
12.4.3 Examples 379
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
13.8.1 Image Background Characterized by Supervised Knowledge 402
13.8.2 Image Background Characterized by Unsupervised Knowledge 405
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
14.3.1 Weighting Matrix Derived from a Parameter Estimation Perspective 414
14.3.1.1 MD-Weighted AC-LSMA 415
14.3.1.2 LCMV-Weighted AC-LSMA 415
14.3.2 Weighting Matrix Derived from Fisher’s Linear Discriminant
Analysis Perspective 416
14.3.3 Weighting Matrix Derived from an Orthogonal Subspace Projection
Perspective 417
14.3.3.1 OSP-Weighted AC-LSMA 417
14.3.3.2 SSP-Weighted AC-LSMA 418
14.4 Synthetic Image-Based Computer Simulations 419
14.5 Real Image Experiments 426
14.6 Conclusions 432
xiv Contents
15 KERNEL-BASED LINEAR SPECTRAL MIXTURE ANALYSIS 434
15.1 Introduction 434
15.2 Kernel-Based LSMA (KLSMA) 436
15.2.1 Kernel Least Squares Orthogonal Subspace Projection (KLSOSP) 436
15.2.2 Kernel-Based Non-Negative Constraint Least Square (KNCLS) 438
15.2.3 Kernel-Based Fully Constraint Least Square (KFCLS) 439
15.2.4 A Note on Kernelization 440
15.3 Synthetic Image Experiments 441
15.4 AVIRIS Data Experiments 444
15.4.1 Radial Basis Function Kernels 449
15.4.2 Polynomial Kernels 452
15.4.3 Sigmoid Kernels 454
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
16.2.1 Euclidean Distance 471
16.2.2 Spectral Angle Mapper 471
16.2.3 Orthogonal Projection Divergence 471
16.2.4 Spectral Information Divergence 471
16.3 Correlation-Weighted Hyperspectral Measures for Target Discrimanition
and Identification 472
16.3.1 Hyperspectral Measures Weighted by A Priori Correlation 473
16.3.1.1 OSP-Based Hyperspectral Measures for Discrimination 473
16.3.1.2 OSP-Based Hyperspectral Measures for Identification 473
16.3.2 Hyperspectral Measures Weighted by A Posteriori Correlation 474
16.3.2.1 Covariance Matrix-Weighted Hyperspectral Measures 474
16.3.2.2 Correlation Matrix-Weighted Hyperspectral Measures 475
16.3.2.3 Covariance Matrix-Weighted Matched Filter Distance 475
16.3.2.4 Correlation Matrix-Weighted Matched Filter Distance 476
16.4 Experiments 477
16.4.1 HYDICE Image Experiments 477
16.4.2 AVIRIS Image Experiments 478
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
17.4.1 LS-ULSMA 491
17.4.2 CA-ULSMA 499
Contents xv
17.5 Real-Image Experiments 503
17.5.1 LS-ULSMA 503
17.5.2 CA-ULSMA 505
17.5.3 Qualitative and Quantitative Analyses between ULSMA and SLSMA 511
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
18.5.1 AVIRIS Image Data 534
18.5.2 DAIS 7915 Image Data 537
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
19.3.1 Dimensionality Reduction by Transform-Based Spectral Compression 550
19.3.1.1 Determination of Number of PCs/ICs to be Retained 551
19.3.1.2 PCA (ICA)/2D Compression 551
19.3.1.3 PCA (ICA)/3D Compression 552
19.3.1.4 Inverse PCA (Inverse ICA)/2D Compression 553
19.3.1.5 Inverse PCA (Inverse PCA)/3D Compression 553
19.3.1.6 Mixed Component Transforms for Hyperspectral Compression 554
19.3.2 Dimensionality Reduction by Band Selection-Based Spectral Compression 556
19.4 Progressive Spectral/Spatial Compression 557
19.5 3D Compression 557
19.5.1 3D-Multicomponent JPEG 557
19.5.2 3D-SPIHT Compression 558
19.6 Exploration-Based Applications 559
19.6.1 Linear Spectral Mixture Analysis 559
19.6.2 Subpixel Target Detection 559
19.6.3 Anomaly Detection 560
19.6.4 Endmember Extraction 561
19.7 Experiments 561
19.7.1 Synthetic Image Experiments 562
19.7.2 Real Image Experiments 567
19.8 Conclusions 580
20 PROGRESSIVE SPECTRAL DIMENSIONALITY PROCESS 581
20.1 Introduction 582
20.2 Dimensionality Prioritization 584
xvi Contents
20.3 Representation of Transformed Components for DP 585
20.3.1 Projection Index-Based PP 585
20.3.2 Mixed Projection Index-Based Prioritized PP (M-PIPP) 587
20.3.3 Projection Index-Based Prioritized PP (PI-PRPP) 587
20.3.4 Initialization-Driven PIPP (ID-PIPP) 588
20.4 Progressive Spectral Dimensionality Process 589
20.4.1 Progressive Principal Components Analysis 591
20.4.1.1 Simultaneous PCA 591
20.4.1.2 Progressive PCA 592
20.4.1.3 Sequential PCA 593
20.4.1.4 Initialization-Driven PCA 595
20.4.2 Progressive High-Order Statistics Component Analysis 596
20.4.3 Progressive Independent Component Analysis 596
20.5 Hyperspectral Compression by PSDP 597
20.5.1 Progressive Spectral Dimensionality Reduction 597
20.5.2 Progressive Spectral Dimensionality Expansion 597
20.6 Experiments for PSDP 598
20.6.1 Endmember Extraction 598
20.6.2 Land Cover/Use Classification 599
20.6.3 Linear Spectral Mixture Analysis 603
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
21.3.1 Second-Order Statistics-Based BPC 617
21.3.1.1 Variance-Based BPC 617
21.3.1.2 Signal-to-Noise-Ratio-Based BPC 618
21.3.2 High-Order Statistics-Based BPC 618
21.3.2.1 Skewness 618
21.3.2.2 Kurtosis 618
21.3.3 Infinite-Order Statistics-Based BPC 618
21.3.3.1 Entropy 619
21.3.3.2 Information Divergence 619
21.3.4 Classification-Based BPC 619
21.3.4.1 Fisher’s Linear Discriminant Analysis (FLDA)-Based BPC 619
21.3.4.2 OSP-Based BPC 620
21.3.5 Constrained Band Correlation/Dependence Minimization 620
21.3.5.1 Band Correlation/Dependence Minimization 621
21.3.5.2 Band Correlation Constraint 622
21.4 Experiments for BP 624
21.4.1 Applications Using Highest-Prioritized Bands 625
21.4.1.1 Unsupervised Linear Spectral Mixture Analysis 626
21.4.1.2 Endmember Extraction 632
21.4.2 Applications Using Least-Prioritized Bands 635
21.4.2.1 Unsupervised Linear Spectral Mixture Analysis 636
21.4.2.2 Endmember Extraction 637
Contents xvii
21.4.3 Applications Using Mixing Highest-Prioritized and Least-Prioritized Bands 646
21.4.3.1 Unsupervised Linear Spectral Mixture Analysis 646
21.4.3.2 Endmember Extraction 646
21.5 Progressive Band Dimensionality Process 651
21.6 Hyperspectral Compresssion by PBDP 653
21.6.1 Progressive Band Dimensionality Reduction Via BP 654
21.6.2 Progressive Band Dimensionality Expansion Via BP 655
21.7 Experiments for PBDP 656
21.7.1 Endmember Extraction 656
21.7.2 Land Cover/Use Classification 658
21.7.3 Linear Spectral Mixture Analysis 660
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
22.4.1 Shannon Coding-Based DDA 667
22.4.2 Huffman Coding-Based DDA 668
22.4.3 Hamming Coding-Based DDA 669
22.4.4 Notes on DDA 669
22.5 Experiments for Dynamic Dimensionality Allocation 669
22.5.1 Reflectance Cuprite Data 670
22.5.2 Purdue’s Data 672
22.5.3 HYDICE Data 674
22.6 Conclusions 682
23 PROGRESSIVE BAND SELECTION 683
23.1 Introduction 683
23.2 Band De-Corrleation 684
23.2.1 Spectral Measure-Based BD 684
23.2.2 Orthogonalization-Based BD 685
23.3 Progressive Band Selection 686
23.3.1 PBS: BP Followed by BD 687
23.3.2 PBS: BD Followed by BP 687
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
xviii Contents
24.2 Binary Coding 720
24.2.1 SPAM Binary Coding 720
24.2.2 Median Partition Binary Coding 721
24.2.3 Halfway Partition Binary Coding 722
24.2.4 Equal Probability Partition Binary Coding 722
24.3 Spectral Feature-Based Coding 723
24.4 Experiments 725
24.4.1 Computer Simulations 725
24.4.2 Real Hyperspectral Image Data 730
24.5 Conclusions 740
25 VECTOR CODING FOR HYPERSPECTRAL SIGNATURES 741
25.1 Introduction 741
25.2 Spectral Derivative Feature Coding 743
25.2.1 Re-interpretation of SPAM and SFBC 743
25.2.2 Spectral Derivative Feature Coding 744
25.2.3 AVIRIS Data Experiments 746
25.2.3.1 Signature Discrimination 747
25.2.3.2 Mixed Signature Classification 748
25.2.4 NIST Gas Data Experiments 749
25.2.4.1 Signature Discrimination 750
25.2.4.2 Mixed Signature Classification 751
25.3 Spectral Feature Probabilistic Coding 755
25.3.1 Arithmetic Coding 755
25.3.2 Spectral Feature Probabilistic Coding 756
25.3.3 AVIRIS Data Experiments 758
25.3.4 NIST Gas Data Experiments 760
25.4 Real Image Experiments 764
25.4.1 SDFC 764
25.4.2 SFPC 766
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
26.3.1 Spectral Discrimination 784
26.3.2 Spectral Identification 785
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
Contents xix
27.2 Orthogonal Subspace Projection-Based Band Prioritization Criterion 801
27.3 Variable-Number Variable-Band Selection 803
27.4 Experiments 806
27.4.1 Hyperspectral Data 806
27.4.1.1 Signature Discrimination 806
27.4.1.2 Signature Classification/Identification 809
27.4.1.3 Noise Effect on VNVBS 811
27.4.2 NIST-Gas Data 813
27.4.2.1 Signature Discrimination 813
27.4.2.2 Signature Classification/Identification 814
27.4.2.3 Signature Discrimination between Two Signatures
with Different Numbers of Bands 816
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
28.3.1 Kalman Filter-based Spectral Signature Estimator 825
28.3.2 Kalman Filter-Based Spectral Signature Identifier 826
28.3.3 Kalman Filter-Based Spectral Signature Quantifier 828
28.4 Computer Simulations Using AVIRIS Data 831
28.4.1 KFSSE 831
28.4.2 KFSSI 832
28.4.2.1 Subpixel Target Identification by KFSSI 832
28.4.2.2 Mixed Target Identification by KFSSI 838
28.4.3 KFSSQ 839
28.4.3.1 Subpixel Target Quantification by KFSSQ 839
28.4.3.2 Mixed Target Quantification by KFSSQ 840
28.5 Computer Simulations Using NIST-Gas Data 843
28.5.1 KFSSE 843
28.5.2 KFSSI 843
28.5.2.1 Subpixel Target Identification by KFSSI 843
28.5.2.2 Mixed Target Identification by KFSSI 848
28.5.3 KFSSQ 849
28.5.3.1 Subpixel Target Identification by KFSSQ 849
28.5.3.2 Mixed Target Quantification by KFSSQ 849
28.6 Real Data Experiments 852
28.6.1 KFSSE 852
28.6.2 KFSSI 852
28.6.3 KFSSQ 856
28.7 Conclusions 857
29 WAVELET REPRESENTATION FOR HYPERSPECTRAL SIGNALS 859
29.1 Introduction 859
29.2 Wavelet Analysis 860
xx Contents
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
29.3.1 Wavelet-Based Signature Characterization Algorithm for Signature
Self-Tuning 863
29.3.2 Wavelet-Based Signature Characterization Algorithm for Signature
Self-Correction 866
29.3.3 Signature Self-Discrimination, Classification, and Identification 867
29.4 Synthetic Image-Based Computer Simulations 868
29.4.1 Signature Self-Tuning and Self-Denoising 869
29.4.2 Signature Self-Discrimination, Self-Classification, and
Self-Identification 870
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
30.3.1 Synthetic Image Experiments 881
30.3.2 HYDICE Image Experiments 886
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 TOMULTISPECTRAL
IMAGERY 897
31.1 Introduction 897
31.2 Band Dimensionality Expansion 899
31.2.1 Rationale for Developing BDE 899
31.2.2 Band Expansion Process 901
31.3 Hyperspectral Imaging Techniques Expanded by BDE 902
31.3.1 BEP-Based Orthogonal Subspace Projection 903
31.3.2 BEP-Based Constrained Energy Minimization 903
31.3.3 BEP-Based RX-Detector 903
31.4 Feature Dimensionality Expansion by Nonlinear Kernels 904
31.4.1 FDE by Transformation 905
31.4.2 FDE by Classification 907
31.4.2.1 FDE by Classification using Sample Spectral Correlation 907
31.4.2.2 FDE by Classification using Intrapixel Spectral Correlation 908
31.5 BDE in Conjunction with FDE 909
31.6 Multispectral Image Experiments 909
31.7 Conclusion 918
Contents xxi
32 MULTISPECTRAL MAGNETIC RESONANCE IMAGING 920
32.1 Introduction 920
32.2 Linear Spectral Mixture Analysis for MRI 923
32.2.1 Orthogonal Subspace Projection to MRI 925
32.2.2 Band Expansion Process-Based OSP 927
32.2.3 Unsupervised Orthogonal Subspace Projection 928
32.3 Linear Spectral Random Mixture Analysis for MRI 928
32.3.1 Source Separation-Based OC-ICA for MR Image Analysis 930
32.3.2 Band Expansion Process Over complete ICA for MR Image Analysis 931
32.3.2.1 Eigenvector-Prioritized ICA 931
32.3.2.2 High-Order Statistics-Based PICA 932
32.3.2.3 ATGP-Prioritized PCA 932
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
33.1.1 Pigeon-Hole Principle 956
33.1.1.1 Multispectral Imagery Versus Hyperspectral Imagery 957
33.1.1.2 Virtual Dimensionality 957
33.1.2 Principle of Orthogonality 963
33.2 Endmember Extraction 964
33.3 Linear Spectral Mixture Analysis 970
33.3.1 Supervised LSMA 970
33.3.2 Unsupervised LSMA 973
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
33.7.1 Signal Coding 986
33.7.2 Signal Estimation 986
33.8 Applications 987
33.9 Further Topics 987
33.9.1 Causal Processing 987
33.9.2 Real-Time Processing 988
33.9.3 FPGA Designs for Hardware Implementation 989
33.9.4 Parallel Processing 990
33.9.5 Progressive Hyperspectral Processing 990
GLOSSARY 993
APPENDIX: ALGORITHM COMPENDIUM 997
REFERENCES 1052
INDEX 1071
xxii Contents
Preface
Hyperspectral imaging has witnessed tremendous growth over the past few years. Still its applica-
tions to new areas are yet to be explored. Many hyperspectral imaging techniques have been devel-
oped and reported in various venues. My first book, Hyperspectral Imaging: Techniques for
Spectral Detection and Classification, referenced as Chang (2003a), was written in an attempt to
summarize the research conducted at that time in my laboratory (remote sensing signal and image
processing laboratory, RSSIPL) and to provide readers with a peek of this fascinating and exciting
area. With rapid advancement in this area many signal processing techniques have been developed
for hyperspectral signal and image processing. This book has been written with four goals in mind.
One is to continuously explore new statistical signal processing algorithms in this area for various
applications. Many results in this book are new, particularly some in Chapters 2, 4, 5–6, 11, 16, 18–
19, 23, 24, 29, 30–31, and 33. A second goal is to supplement Chang (2003a), where many poten-
tial research efforts were only briefly mentioned (in Chapter 18 of the book). A third goal is to
distinguish this book from Chang (2003a) in many ways. Unlike Chang (2003a) where the main
theme was hyperspectral target detection and classification from a viewpoint of subpixel and mixed
pixel analysis, this book is focused on more in-depth treatment of hyperspectral signal and image
processing from a statistical signal processing point of view. A fourth and last goal is to focus on
several unsettled but very important issues that have been avoided and never addressed in the past.
One issue is “how many spectral signatures are required to unmix data?” arising in linear hyper-
spectral unmixing. This has been a long-standing and unresolved issue in remote sensing image
processing, specifically hyperspectral imaging, since the number of signatures to be used for data
unmixing has a significant impact on image analysis while its accurate number is never known in
real applications. Another is “how many pure spectral signatures, referred to as endmembers, are
supposed to be present in the data to be processed?” It is common practice to assume that the
number of signatures used for spectral unmixing is the same number of endmembers.
Unfortunately, such a claim, which has been widely accepted by the community, is not always true
in practical applications (see Chapter 17). The issue of endmembers has not received much interest
in multispectral image analysis because of its low spectral and spatial resolutions that generally
result in mixed data sample vectors. However, due to recent advances in hyperspectral imaging
sensors with hundreds of contiguous spectral bands endmember extraction has become increas-
ingly important since endmembers provide crucial “nonliteral” information in spectral interpreta-
tion, characterization, and analysis. Interestingly, this issue has never been seriously addressed
until recently when it has been investigated by a series of papers (Chang, 2006ab; Chang and
xxiii
Plaza, 2006; Chang et al., 2006; Plaza and Chang 2006) by introducing a new concept of virtual
dimensionality (VD). Besides, some controversial issues result from misinterpreting VD. There-
fore, one of the major chapters in this book is Chapter 5, which revisits VD to explore its utility in
various applications. Unlike the intrinsic dimensionality (ID), also known as effective dimension-
ality (ED), which is somewhat abstract and defined as the minimum number of parameters to rep-
resent general high-dimensional multivariate data, VD is more practical and realistic. It is defined
as the number of “spectrally” distinct signatures particularly developed for hyperspectral data in
which the non-literal (spectral) information is more crucial and vital than information provided by
other dimensions such as spatial information. In particular, an issue arises in how to define the
spectral distinction among signatures in VD estimation. Furthermore, unlike ID that is a one-size-
fits-all definition for all data sets, VD should adapt to data sets used for different applications as
well as vary with the techniques used to estimate VD. In order to address this issue, Chapter 5
explores two types of VD criteria, data characterization-driven criteria and data representation-
driven criteria, to define spectrally distinct signatures, and further decouples the concept of VD
from the techniques used to estimate VD. Consequently, when VD is poorly estimated by one tech-
nique for a particular data set, it is not the definition of VD to be blamed, but rather the technique
used for VD estimation that is not applicable to this particular data set. In addition, an issue related
to VD is “characterization of pixel information.” For example, an anomaly is not necessarily an
endmember and vice versa. So, the issues “what is the distinction between these two?” and “how
do we characterize these two?” become interesting issues in hyperspectral data exploitation to
be discussed in Chapter 18.
Another interesting topic presented in this book is a new concept of “hyperspectral information
compression” introduced in Chapters 19–23. It is different from the commonly used so-called
hyperspectral data compression in the sense that hyperspectral information compression is gener-
ally performed based on the information required to be retained rather than the size of hyperspec-
tral data to be compressed. Therefore, a more appropriate term to be used is “exploitation-based
lossy hyperspectral data compression.” Nevertheless, it should be noted that the definitions
and terminologies used in these chapters are by no means standard.
Finally, an issue of “multispectral imagery versus hyperspectral imagery” is also investi-
gated. It seems that there is no cut-and-dried definition to distinguish these two terminologies.
A general understanding of distinguishing these two is that a hyperspectral image is acquired
by hundreds of contiguous spectral channels/bands with very high spectral resolution, while a
multispectral image is collected by tens of discrete spectral channels/bands with low spectral
resolution. If this interpretation is used, we run into a dilemma, “how many spectral channels/
bands are enough for a remotely sensed image to be called a hyperspectral image?” or “how
fine the spectral resolution should be for a remote sensing image to be considered as a hyper-
spectral image?” For example, if we take a small set of hyperspectral band images with spec-
tral resolution 10 nm, say five spectral band images, to form a five-dimensional image cube, do
we still consider this new-formed five-dimensional image cube as a hyperspectral image or
simply a multispectral image? If we adopt the former definition based on the number of bands,
this five-dimensional image cube should be viewed as a multispectral image. On the other
hand, if we adopt the latter definition based on spectral resolution, the five-dimensional image
cube should be considered as a hyperspectral image. Thus far, it seems that there is no general
consensus on this issue. In Chapter 31, an attempt is made to address this issue from a view-
point of how two versions of independent component analysis (ICA), over-complete ICA, and
under-complete ICA can be used to resolve this long-debated issue in the context of linear
spectral mixture analysis (LSMA). After all, some of these issues may never be settled or
standardized for years to come. Many researchers can always argue differently at their
xxiv Preface
discretion and provide their own versions of interpretation. I have no intention of disputing any
of them, but rather respect their opinions.
Since processing hyperspectral signatures as one-dimensional signals and processing hyper-
spectral images as three-dimensional image cubes are rather different, this book makes a distinc-
tion by treating hyperspectral image processing and hyperspectral signal processing in two
separate categories to avoid confusion. To this end, three categories are specifically outlined in this
book: Category A: hyperspectral image processing; Category B: hyperspectral signal processing;
and Category C: applications.
For better understanding, a set of six chapters is included in PART I as preliminaries that
cover fundamentals and provide a basic background required for readers to follow algorithm
design and development discussed in this book. Category A is made up of 15 chapters (Chap-
ters 7–23) treated separately in four different parts, Part II to Part V. Category B consists of
six chapters (Chapters 24–29) in two separate parts, Part VI and Part VII. Finally, applica-
tions make up Category C.
It is worth noting that many materials presented in this book have been only available
after Chang (2003a). Theses include endmember extraction (Chapters 7–11), algorithm design
using different levels of information (supervised linear hyperspectral mixture analysis in
Chapters 12–15), pixel characterization and analysis (unsupervised hyperspectral analysis in
Chapters 16–18), exploitation-based hyperspectral information compression (Chapters 19–23),
hyperspectral signature coding and characterization (Chapters 24–29), and applications
(Chapters 30–32) in Category C.
There are three unique features in this book that cannot be found in Chang (2003a): (1) Part I:
preliminaries (Chapters 2–6); (2) extensive studies of synthetic image-based experiments for per-
formance evaluation; and (3) an appendix on algorithm compendium that compiles recently devel-
oped signal processing algorithms developed in the RSSIPL, all of which are believed to be useful
and beneficial to those who design and develop algorithms for hyperspectral signal/image process-
ing. Because this book also addresses many issues that were not explored in Chang (2003a), it can
be used in conjunction with Chang (2003a) without much overlap, where the latter provides neces-
sary basic background in design and development of statistical signal processing algorithms for
hyperspectral image analysis, especially for subpixel detection and mixed pixel classification.
Therefore, on one end, those who have been involved in hyperspectral imaging and are familiar
with hyperspectral imaging techniques will find this book useful as reference material. On the
other end, those who are new will find this book a good and valuable guide on the topics that may
interest them.
I would like to thank the Spectral Information Technology Applications Center (SITAC) that
provides its HYDICE data to be used for experiments in this book. I would also like to acknowl-
edge the use of Purdue’s Indiana Indian Pine test site and the AVIRIS Cuprite image data website.
I owe my sincere gratitude and deepest appreciation to my former Ph.D. students, Drs.
Sumit Chakravarty, Hsian-Min Chen, Yingzi Du, Qian Du, Mingkai Hsueh, Baohoing Ji,
Xiaoli Jiao, Keng-Hao Liu, Weimin Liu, Bharath Ramakrishna, Hsuan Ren, Haleh Safavi,
Chiun-Mu Wang, Jianwei Wang, Jing Wang, Su Wang, Englin Wong, Chao-Cheng Wu, Wei
Xiong, and MS student, Ms. Farzeen Chaudhary as well as my current Ph.D. student, Shih-Yu
Chen. My appreciation is also extended to my colleagues, Professor Chinsu Lin with the
Department of Forestry and Natural Resources at National Chiayi University, Dr. Ching Wen
Yang who is the Director of Computer Center, Taichung Veterans General Hospital, and Pro-
fessor Ching Tsorng Tsai with the Computer Science Department at Tunghai University. I
would like to thank particularly my former Ph.D. students, Dr. Chao-Cheng Wu who carried
out most of the experiments presented in Chapters 7–11, Dr. Ken-Hao Liu who performed
Preface xxv
many experiments described in Chapters 21–23, Dr. Su Wang who did all the work men-
tioned in Chapter 29, Dr. Englin Wong who performed all the experiments described in
Chapter 32, and Professor Antonio J. Plaza who contributed to some part of Chapter 18
when he was on sabbatical leave from the Computer Science Department, University of
Extremadura, Spain, in 2004 to visit my laboratory. This book could not have been com-
pleted without their contributions.
I would also like to thank the Ministry of Education in Taiwan for supporting me as a Distin-
guished Lecture Chair within the Department of Electrical Engineering from 2005 to 2006, a
Chair Professorship of Reduction Technology within the Environmental Restoration and Disaster
Reduction Research Center and Department of Electrical Engineering from 2006 to 2009, and a
Chair Professorship of Remote Sensing Technology within the Department of Electrical Engi-
neering from 2009 to 2012, at National Chung Hsing University where Professor Yen-Chieh
Ouyang of Electrical Engineering has been a very supportive host during my visit. In particular,
during the period 2009–2010, I was on sabbatical leave from UMBC to visit National Chung
Hsing University where my appointment as a distinguished visiting fellow/fellow professor was
supported and funded by the National Science Council in Taiwan under projects of NSC 98-
2811-E-005-024 and NSC 98-2221-E-005-096. All their support is highly appreciated.
Last but not least, I would also like to thank my friends, Dr. San-Kan Lee (Deputy Superintend-
ent of Taichung Veterans General Hospital (TCVGH)), Dr. Clayton Chi-Chang Chen (Chairman of
Radiology at TCVGH), Dr. Jyh-Wen Chai (Section Chief of Radiology at TCVGH), and Dr. Yong
Kie Wong (Head of Dental Department at TCVGH) who have selflessly provided their expertise
and resources, especially an excellent testbed environment to help me use hyperspectral imaging
techniques in magnetic resonance imaging (MRI). Chapter 32 is indeed a culmination of such a
great working relationship.
As a final note, I would like to share that this book was supposed to be delivered by 2008. The
most important factor that caused the delay is the urge to include the latest reports on hyperspectral
data analysis. It is very difficult and challenging to keep a track of such new developments.
Nevertheless, this book has grown three times larger than what I had originally proposed. Those
who are interested in my forthcoming 2013 book can have a quick peek of these topics briefly
discussed in Chapter 33, which includes a new development of target-characterized virtual
dimensionality (VD), real-time and progressive processing of endmember extraction, unsupervised
target detection, anomaly detection, as well as their field programmable gate array (FPGA)
implementation.
CHEIN-I CHANG ( )
Professor of Electrical Engineering
Remote Sensing Signal and Image Processing Laboratory (RSSIPL)
University of Maryland, Baltimore County
Baltimore, Maryland
USA
Chair Professor of Remote Sensing Technology
National Chung Hsing University
Taichung, Taiwan
Republic of China
xxvi Preface
Distinguished Professor
Providence University
Taichung, Taiwan
Republic of China
International Chair Professor
National Taipei University of Technology
Taipei, Taiwan
Republic of China
Technical Advisor
Center for QUantitative Imaging in Medicine (CQUIM)
Taichung Veterans General Hospital
Taichung, Taiwan
Republic of China
Fall 2012
Preface xxvii