Chang Ch.-I. Hyperspectral data processing: algorithm design and analysis (Hoboken, 2013). - ОГЛАВЛЕНИЕ / CONTENTS

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ОбложкаChang Ch.-I. Hyperspectral data processing: algorithm design and analysis. - Hoboken: Wiley, 2013. - xxvii, 1135 p.: ill. - (A comprehensive reference on advanced hyperspectral imaging). - Bibliogr.: p.1052-1069. - Ind.: p.1071-1135. - ISBN 978-0-471-69056-6
 

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Оглавление / Contents
 
PREFACE ..................................................... xxiii

1    OVERVIEW AND 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  AVIRISData ......................................... 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
     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 (СЕМ) ...................................... 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  Biometrie Recognition .............................. 95
3.7  Conclusions ............................................... 99

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 (ТЕ) ...... 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 ТЕ3 (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 Co variance
         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
     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/НуSime ................. 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 (ТЕ) 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 1СA-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
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 ТЕЗ (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
     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 VC A (RVC A) ...................................... 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
     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 ТЕЗ 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 δ1.SαP(r) and Least-Squares
            Linear Spectral Mixture 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
12.4 OSP Implemented with Partial Knowledge ................... 372
     12.4.1 СЕМ ............................................... 373
       12.4.1.1 d Is Orthogonal to U (i.e., P 1/U = d) and
         R = I (i.e., Spectral Correlation is Whitened) ....... 374
       12.4.1.2 An Alternative Approach to Implementing СЕМ ... 374
       12.4.1.3 СЕМ Implemented in Conjunction with P 1/U ..... 375
       12.4.1.4 СЕМ Implemented in Conjunction with P 1/U in 
         White Noise .......................................... 376
     12.4.2 TCIMF ............................................. 377
       12.4.2.1 D = mp = d with nD = 1 and U = [m1, m2 ... 
         mp-1] with nU = p - 1 ................................ 378
       12.4.2.2 D = mp = d with nD = 1 and U = [m1, m2 ... 
         mp-1] with nU = p - 1 and 
         R = I ................................................ 378
       12.4.2.3 D = d and U = 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 СЕМ .. 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

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 Discrimination 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
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 ULSMA Versus 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
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 PC A ............................. 591
       20.4.1.2 Progressive PC A .............................. 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-BasedBPC .................................. 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
     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 PB DP .................................... 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 DIMENSIONALITY ALLOCATION ........................ 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-correlation ...................................... 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
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-NUMBER VARIABLE-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
     27.4.1 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-GasData ...................................... 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 Kaiman Filter-Based Linear Unmixing ...................... 822
28.3 Kaiman Filter-Based Spectral Characterization Signal-
     Processing Techniques .................................... 824
     28.3.1 Kaiman Filter-based Spectral Signature Estimator .. 825
     28.3.2 Kaiman Filter-Based Spectral Signature
       Identifier ............................................. 826
     28.3.3 Kaiman 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
     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 TO MULTISPECTRAL
     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

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


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Посещение N 789 c 10.11.2015