Hyperspectral Imaging

Techniques for Spectral Detection and Classification

By: Chein-I Chang

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Hardcover | 31 July 2003 | Edition Number 2

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396 Pages

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23.39 x 15.6 x 2.24

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Hyperspectral Imaging: Techniques for Spectral Detection and Classification is an outgrowth of the research conducted over the years in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. It explores applications of statistical signal processing to hyperspectral imaging and further develops non-literal (spectral) techniques for subpixel detection and mixed pixel classification. This text is the first of its kind on the topic and can be considered a recipe book offering various techniques for hyperspectral data exploitation. In particular, some known techniques, such as OSP (Orthogonal Subspace Projection) and CEM (Constrained Energy Minimization) that were previously developed in the RSSIPL, are discussed in great detail. This book is self-contained and can serve as a valuable and useful reference for researchers in academia and practitioners in government and industry.

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Introduction	p. 1
Background	p. 2
Outline of the Book	p. 3
Stochastic Hyperspectral Measures	p. 3
Subpixel Detection	p. 4
Mixed Pixel classification (MPC)	p. 5
Unconstrained MPC	p. 7
Constrained MPC	p. 7
Automatic Mixed Pixel Classification (AMPC)	p. 8
Hyperspectral Data to be Used in the Book	p. 8
Notations to Be Used in the Book	p. 10
Hyperspectral Measures	p. 13
Hyperspectral Measures for Spectral Characterization	p. 15
Measures of Spectral Variability	p. 15
Spectral Information Measure (SIM)	p. 16
Hidden Markov Model(HMM)-Based Measure	p. 17
Spectral Similarity Measures	p. 20
Commonly Used Measures	p. 20
Distance-Based Measures	p. 20
Orthogonal Projection-Based Measures	p. 20
Spectral Information Divergence (SID)	p. 21
Hidden Markov Model-Based Information Divergence (HMMID)	p. 23
Measures of Spectral Discriminability	p. 23
Relative Spectral Discriminatory ProBability (RSDPB)	p. 24
Relative Spectral Discriminatory PoWer (RSDPW)	p. 24
Relative Spectral Discriminatory Entropy (RSDE)	p. 25
Experiments	p. 26
AVIRIS Data	p. 26
HYDICE Data	p. 31
Conclusions	p. 34
Subpixel Detection	p. 37
Target Abundance-Constrained Subpixel Detection: Partially Constrained Least-Squares Methods	p. 39
Introduction	p. 39
Linear Spectral Mixture Model	p. 40
Orthogonal Subspace Projection (OSP)	p. 41
Sum-to-one Constrained Least Squares Method (SCLS)	p. 44
Nonnegativity Constrained Least Squares Method (NCLS)	p. 45
Hyperspectral Image Experiments	p. 48
Conclusions	p. 50
Target Signature-Constrained Subpixel Detection: Linearly Constrained Minimum Variance (LCMV)	p. 51
Introduction	p. 51
LCMV Target Detector	p. 53
Constrained Energy Minimization (CEM)	p. 54
Target-Constrained Interference-Minimized Filter (TCIMF)	p. 55
Relationship Among Osp, Cem and Tcimf	p. 56
A Comparartive Analysis Between Cem and Tcimf	p. 58
Computer Simulations	p. 58
Hyperspectral Image Experiments	p. 61
Sensitivity of Cem and Tcimf to Level of Target Information	p. 63
Computer Simulations	p. 64
Hyperspectral Image Experiments	p. 67
Real-Time Processing	p. 68
Conclusions	p. 71
Automatic Subpixel Detection: Unsupervised Subpixel Detection	p. 73
Introduction	p. 73
Unsupervised Vector Quantization (UVQ)-Based Algorithm	p. 74
Unsupervised Target Generation Process (UTGP)	p. 75
Unsupervised Ncls (UNCLS) Algorithm	p. 78
Experiments	p. 80
Conclusions	p. 87
Automatic Subpixel Detection: Anomaly Detection	p. 89
Introduction	p. 89
RXD	p. 91
Lptd and Utd	p. 94
Relationship Between Cem and Rxd	p. 97
Real-Time Processing	p. 99
Conclusions	p. 102
Sensitivity of Subpixel Detection	p. 105
Introduction	p. 105
Sensitivity of Target Knowledge	p. 107
Sensitivity of Noise	p. 111
Tscsd	p. 111
Hyperspectral Image Experiments	p. 116
AVIRIS Data	p. 116
HYDICE Data	p. 125
Sensitivity of Anomaly Detection	p. 129
Conclusions	p. 137
Unconstrained Mixed Pixel Classification	p. 139
Unconstrained Mixed Pixel Classification: Least-Squares Subspace Projection	p. 141
Introduction	p. 141
A Posteriori Osp	p. 144
Signature Subspace Projection (SSP) Classifier	p. 144
Target Subspace Projection (TSP) Classifier	p. 146
Oblique Subspace Projection (OBSP) Classifier	p. 147
Unconstrained Maximum Likelihood Estimation Classifier	p. 148
Estimation Error Evaluated by Roc Analysis	p. 150
Signature Subspace Projection (SSP) Classifier	p. 151
Oblique Subspace Projection (OBSP) Classifier	p. 153
Computer Simulations and Hyperspectral Image experiments	p. 153
Computer Simulations	p. 154
Hyperspectral Data	p. 156
Conclusions	p. 159
A Quantitative Analysis of Mixed-to-Pure Pixel Conversion (MPCV)	p. 161
Introduction	p. 162
Conversion of MPC to PPC	p. 162
Mixed-to-Pure Pixel Converter (MPCV)	p. 163
Minimum Distance-Based Classification	p. 164
Fisher's Linear Discriminant Analysis (LDA)	p. 166
Unsupervised Classification	p. 169
Criteria for Target Detection and Classification	p. 169
Comparative Performance Analysis	p. 171
Conclusions	p. 177
Constrained Mixed Pixel Classification	p. 179
Target Abundance-Constrained Mixed Pxiel Classification (TACMPC)	p. 181
Introduction	p. 181
Fully Constrained Least-Squares Approach	p. 183
Fully Constrained Least-Squares Method (FCLS)	p. 183
Unsupervised FCLS Method (UFCLS)	p. 183
Modified Fully Constrained Least-Squares (MFCLS) Approach	p. 184
Computer Simulations and Real Hyperspectral Image Experiments	p. 186
Computer Simulations	p. 186
AVIRIS Image Experiments	p. 188
HYDICE Image Experiments	p. 193
Near Real-Time Implementation	p. 201
Conclusions	p. 205
Target Signature-Constrained Mixed Pixel Classification (TSCMPC): LCMV Classifiers	p. 207
Introduction	p. 207
LCMV Classifer	p. 208
Bowles Et Al.'s Filter Vectors (FV) Algorithm	p. 209
Color Assignment of LCMV Classifiers	p. 211
Extension of Cem Filter to Classifiers	p. 213
Winner-Take-All CEM (WTACEM) Classifier	p. 213
Sum CEM (SCEM) Classifier	p. 213
Multiple-Target CEM (MTCEM) Classifier	p. 213
Target-Constrained Interference-Minimized (TCIM) Classifier	p. 214
Computer Simulations	p. 214
Hyperspectral Image Experiments	p. 218
Real-Time Implementation for LCMV Classifiers	p. 223
Conclusions	p. 227
Target Signature-Constrained Mixed Pixel Classification (TSCMPC): Linearly Constrained Discriminant Analysis (LCDA)	p. 229
Introduction	p. 229
LCDA	p. 230
Whitening Process for LCDA	p. 233
Bowles Et Al.'s Filter Vectors (FV) Algorithm	p. 234
Computer Simulations and Hyperspectral Image Experiments	p. 235
Conclusions	p. 240
Automatic Mixed Pixel Classification (AMPC)	p. 243
Automatic Mixed Pixel Classification (AMPC): Unsupervised Mixed Pixel Classification	p. 245
Introduction	p. 245
Unsupervised MPC	p. 246
Desired Target Detection and Classification	p. 246
Automatic Target Detection and Classification	p. 253
Conclusions	p. 255
Automatic Mixed Pixel Classification (AMPC): Anomaly Classification	p. 257
Introduction	p. 257
Target Discrimination Measures	p. 258
Anomaly Classification	p. 260
Automatic Thresholding Method	p. 260
Analysis on Target Correlation Using Target Discrimination Measures	p. 265
On-Line Implementation	p. 270
Conclusions	p. 274
Automatic Mixed Pixel Classification (AMPC): Linear Spectral Random Mixture Analysis (LSRMA)	p. 277
Introduction	p. 277
Independent Component Analysis (ICA)	p. 279
ICA-Based LSRMA	p. 280
Relative Entropy-Based Measure for ICA	p. 281
Learning Algorithm to Find Separating Matrix W	p. 282
Experiments	p. 284
AVIRIS Image Experiments	p. 284
HYDICE Image Experiments	p. 289
3-D Roc Analysis for LSRMA	p. 295
Conclusions	p. 302
Automatic Mixed Pixel Classification (AMPC): Projection Pursuit	p. 305
Introduction	p. 305
Projection Pursuit	p. 307
Evolutionary Algorithm (EA)	p. 308
Thresholding of Projection Images Using Zero-Detection	p. 310
Experiments	p. 311
AVIRIS Data Experiments	p. 311
HYDICE Data Experiments	p. 313
Conclusions	p. 318
Estimation for Virtual Dimensionality of Hyperspectral Imagery	p. 319
Introduction	p. 319
Neyman-Pearson Detection Theory-Based Eigen-Thresholding Analysis (Hfc Method)	p. 321
Estimation of Noise Covariance Matrix	p. 323
Residual Analysis (Roger, 1996)	p. 323
Inter/Intra-Band Prediction Noise Estimation: Spatial/Spectral Prediction Noise Estimation (Roger and Arnold, 1996)	p. 325
Noise Estimation-Based Eigen-Thresholding	p. 326
Noise-Whitened HFC (NWHFC) Method	p. 326
Noise Subspace Projection (NSP)	p. 326
AIC and MDL	p. 327
Computer Simulations and Hyperspectral Image Experiments	p. 328
Computer Simulations	p. 328
AVIRIS and HYDICE Image Experiments	p. 330
Conclusions	p. 333
Conclusions and Further Techniques	p. 335
Functional Taxonomy of Techniques	p. 335
Mathematical Taxonomy of Techniques	p. 337
Experiments	p. 339
Roc Analysis for Subpixel Detection and Mixed Pixel Classification	p. 340
Sensitivity Issues	p. 341
Sensitivity to Level of Target Information	p. 341
Sensitivity to Noise	p. 341
Real-Time Implementation	p. 341
Further Techniques	p. 342
Generalized Orthogonal Subspace Projection	p. 342
Convex Cone Analysis	p. 343
Kalman Filter-Based Linear Unmixing	p. 344
Interference-Annihilated Eigen-Analysis	p. 344
Band Selection	p. 345
Linear Mixture Analysis-Based Data Compression	p. 346
Radial Basis Function Neural Network Approach	p. 347
Applications to Magnetic Resonance Imaging	p. 347
Glossary	p. 349
References	p. 353
Index	p. 365
Table of Contents provided by Ingram. All Rights Reserved.

Hyperspectral Imaging

Techniques for Spectral Detection and Classification

At a Glance

Hardcover

Shipping

How to return your order

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