Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Volume 1

Front Cover
Springer Science & Business Media, Jul 31, 2003 - Computers - 370 pages
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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Contents

INTRODUCTION
1
11 BACKGROUND
2
12 OUTLINE OF THE BOOK
3
122 Subpixel Detection
4
123 Mixed Pixel Classification MPC
5
1231 Unconstrained MFC
7
1233 Automatic Mixed Pixel Classification AMPC
8
125 Notations to Be Used in the Book
10
95 CONCLUSIONS
177
CONSTRAINED MIXED PIXEL CLASSIFICATION
179
TARGET ABUNDANCECONSTRAINED MIXED PIXEL CLASSIFICATION TACMPC
181
102 FULLY CONSTRAINED LEASTSQUARES APPROACH
183
103 MODIFIED FULLY CONSTRAINED LEASTSQUARES MFCLS APPROACH
184
104 COMPUTER SIMULATIONS AND REAL HYPERSPECTRAL IMAGE EXPERIMENTS
186
1042 AVIKIS Image Experiments
188
1043 HYDICE Image Experiments
193

HYPERSPECTRAL MEASURES
13
HYPERSPECTRAL MEASURES FOR SPECTRAL CHARACTERIZATION
15
211 Spectral Information Measure SIM
16
212 Hidden Markov Model HMMBased Measure
17
22 SPECTRAL SIMILARITY MEASURES
20
222 Spectral Information Divergence SID
21
223 Hidden Markov ModelBased Information Divergence HMMID
23
231 Relative Spectral Discriminatory ProBability RSDPB
24
233 Relative Spectral Discriminatory Entropy RSDE
25
24 EXPERIMENTS
26
242 HYDICE Data
31
25 CONCLUSIONS
34
SUBPIXEL DETECTION
37
TARGET ABUNDANCECONSTRAINED SUBPIXEL DETECTION PARTIALLY CONSTRAINED LEASTSQUARES METHODS
39
32 LINEAR SPECTRAL MIXTURE MODEL
40
33 ORTHOGONAL SUBSPACE PROJECTION OSP
41
34 SUMTOONE CONSTRAINED LEASTSQUARES METHOD SCLS
44
35 NONNEGATTVELY CONSTRAINED LEASTSQUARES METHOD NCLS
45
36 HYPERSPECTRAL IMAGE EXPERIMENTS
48
37 CONCLUSIONS
50
TARGET SIGNATURECONSTRAINED SUBPIXEL DETECTION LINEARLY CONSTRAINED MINIMUM VARIANCE LCMV
51
42 LCMV TARGET DETECTOR
53
421 Constrained Energy Minimization CEM
54
422 TargetConstrained InterferenceMinimized Filter TCIMF
55
43 RELATIONSHIP AMONG OSP CEM AND TCIMF
56
44 A COMPARATIVE ANALYSIS BETWEEN CEM AND TCIMF
58
442 Hyperspectral Image Experiments
61
45 SENSITIVITY OF CEM AND TCIMF TO LEVEL OF TARGET INFORMATION
63
451 Computer Simulations
64
452 Hyperspectral Image Experiments
67
46 REALTIME PROCESSING
68
47 CONCLUSIONS
71
AUTOMATIC SUBPIXEL DETECTION UNSUPERVISED SUBPIXEL DETECTION
73
52 UNSUPERVISED VECTOR QUANTIZATION UVQBASED ALGORITHM
74
53 UNSUPERVISED TARGET GENERATION PROCESS UTGP
75
54 UNSUPERVISED NCLS UNCLS ALGORITHM
78
55 EXPERIMENTS
80
56 CONCLUSIONS
87
AUTOMATIC SUBPEXEL DETECTION ANOMALY DETECTION
89
62 RXD
91
63 LPTD AND UTD
94
64 RELATIONSHIP BETWEEN CEM AND RXD
97
65 REALTIME PROCESSING
99
66 CONCLUSIONS
102
SENSITIVITY OF SUBPIXEL DETECTION
105
72 SENSITIVITY OF TARGET KNOWLEDGE
107
73 SENSITIVITY OF NOISE
111
732 Hyperspectral Image Experiments
116
7322 HYDICE Data
125
74 SENSITIVITY OF ANOMALY DETECTION
129
75 CONCLUSIONS
137
UNCONSTRAINED MIXED PIXEL CLASSIFICATION
139
UNCONSTRAINED MIXED PIXEL CLASSIFICATION LEASTSQUARES SUBSPACE PROJECTION
141
82 A POSTERIORI OSP
144
822 Target Subspace Projection TSP Classifier
146
823 Oblique Subspace Projection OBSP Classifier
147
824 Unconstrained Maximum Likelihood Estimation Classifier
148
83 ESTIMATION ERROR EVALUATED BY ROC ANALYSIS
150
831 Signature Subspace Projection SSP Classifier
151
832 Oblique Subspace Projection OBSP Classifier
153
841 Computer Simulations
154
842 Hyperspectral Data
156
85 CONCLUSIONS
159
A QUANTITATIVE ANALYSIS OF MIXEDTOPURE PIXEL CONVERSION MPCV
161
92 CONVERSION OF MFC TO PPC
162
921 MixedtoPure Pixel Converter MPCV
163
922 Minimum DistanceBased Classification
164
923 Fishers Linear Discriminant Analysis LDA
166
924 Unsupervised Classification
169
94 COMPARATIVE PERFORMANCE ANALYSIS
171
105 NEAR REALTIME IMPLEMENTATION
201
106 CONCLUSIONS
205
TARGET SIGNATURECONSTRAINED MIXED PIXEL CLASSIFICATION TSCMPC LCMV CLASSIFIERS
207
112 LCMV CLASSIFIERS
208
113 BOWLES ET ALS FILTER VECTORS FV ALGORITHM
209
114 COLOR ASSIGNMENT OF LCMV CLASSIFIERS
211
115 EXTENSION OF CEM FILTER TO CLASSIFIERS
213
1154 TargetConstrained InterferenceMinimized TCIM Classifier
214
117 HYPERSPECTRAL IMAGE EXPERIMENTS
218
118 REALTIME IMPLEMENTATION FOR LCMV CLASSIFIERS
223
119 CONCLUSIONS
227
TARGET SIGNATURECONSTRAINED MIXED PIXEL CLASSIFICATION TSCMPC LINEARLY CONSTRAINED DISCRIMINANT ANALYSIS ...
229
122 LCDA
230
123 WHITENING PROCESS FOR LCDA
233
124 BOWLES ET ALS FILTER VECTORS FV ALGORITHM
234
125 COMPUTER SIMULATIONS AND HYPERSPECTRAL IMAGE EXPERIMENTS
236
126 CONCLUSIONS
241
AUTOMATIC MIXED PIXEL CLASSIFICATION AMPC
243
AUTOMATIC MIXED PIXEL CLASSIFICATION AMPC UNSUPERVISED MIXED PIXEL CLASSIFICATION
245
132 UNSUPERVISED MFC
246
134 AUTOMATIC TARGET DETECTION AND CLASSIFICATION
253
135 CONCLUSIONS
255
AUTOMATIC MIXED PIXEL CLASSIFICATIO AMPC ANOMALY CLASSIFICATION
257
142 TARGET DISCRIMINATION MEASURES
258
143 ANOMALY CLASSIFICATION
260
145 ANALYSIS ON TARGET CORRELATION USING TARGET DISCRIMINATION MEASURES
265
146 ONLINE IMPLEMENTATION
270
147 CONCLUSIONS
274
AUTOMATIC MIXED PIXEL CLASSIFICATION AMPC LINEAR SPECTRAL RANDOM MIXTURE ANALYSIS LSRMA
277
152 INDEPENDENT COMPONENT ANALYSIS ICA
279
153 ICABASED LSRMA
280
1531 Relative EntropyBased Measure for ICA
281
1532 Learning Algorithm to Find Separating Matrix W
282
154 EXPERIMENTS
284
1542 HYDICE Image Experiments
289
155 3D ROC ANALYSIS FOR LSRMA
295
156 CONCLUSIONS
302
AUTOMATIC MIXED PIXEL CLASSIFICATION AMPC PROJECTION PURSUIT
305
162 PROJECTION PURSUIT
307
163 EVOLUTIONARY ALGORITHM EA
308
164 THRESHOLDING OF PROJECTION IMAGES USING ZERODETECTION
310
165 EXPERIMENTS
311
1652 HYDICE Data Experiments
313
166 CONCLUSIONS
318
ESTIMATION FOR VIRTUAL DIMENSIONALITY OF HYPERSPECYRAL IMAGERY
319
172 NEYMANPEARSON DETECTION THEORYBASED EIGEN THRESHOLDING ANALYSIS HFC METHOD
321
173 ESTIMATION OF NOISE COVARIANCE MATRIX
324
1731 Residual Analysis Roger 1996
325
SpatialSpectral Prediction Noise Estimation Roger and Arnold 1996
326
174 NOISE ESTIMATIONBASED EIGENTHRESHOLDING
327
175 OTHER METHODS FOR FINDING VD
329
1752 Malinowskis Empirical Indication Function EIF Method
330
1762 AVIRIS and HYDYCE Image Experiments
333
177 CONCLUSIONS
336
CONCLUSIONS AND FURTHER TECHNIQUES
339
182 MATHEMATICAL TAXONOMY OF TECHNIQUES
341
183 EXPERIMENTS
343
184 ROC ANALYSIS FOR SUBPIXEL DETECTION AND MIXED PIXEL CLASSIFICATION
344
185 SENSITIVITY ISSUES
345
187 FURTHER TECHNIQUES
346
1872 Convex Cone Analysis
347
1873 Kalman FilterBased Linear Unmixing
348
1875 Band Selection
349
1876 Linear Mixture AnalysisBased Data Compression
350
1877 Radial Basis Function Neural Network Approach
351
GLOSSARY
353
REFERENCES
357
INDEX
369
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