Hyperspectral imaging: techniques for spectral detection and classification
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
New York [u.a.]
Kluwer Academic/Plenum Publishers
2003
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010036394&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XVI, 370 S. Ill., graph. Darst. |
| ISBN: | 0306474832 |
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| 650 | 4 | |a Multispectral photography | |
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Datensatz im Suchindex
| DE-BY-TUM_call_number | 0002 BAU 967f 2006 A 7275 0102 BAU 967f 2006 A 3311 |
|---|---|
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| adam_text | Titel: Hyperspectral imaging
Autor: Chang, Chein-I.
Jahr: 2003
CONTENTS
1 INTRODUCTION................................................................................1
1.1 BACKGROUND.......................................................................2
1.2 OUTLINE OF THE BOOK..........................................................3
1.2.1 Stochastic Hyperspectral Measures.....................................3
1.2.2 Subpixel Detection........................................................4
1.2.3 Mixed Pixel classification (MPC).....................................5
1.2.3.1 Unconstrained MPC............................................7
1.2.3.2 Constrained MPC...............................................7
1.2.3.3 Automatic Mixed Pixel Classification (AMPC)......8
1.2.4 Hyperspectral Data tobe Used in the Book........................8
1.2.5 Notations to Be Used in the Book...................................10
PART I: HYPERSPECTRAL MEASURES................................................13
2 HYPERSPECTRAL MEASURES FOR SPECTRAL
CHARACTERIZATION.....................................................................15
2.1 MEASURES OF SPECTRAL VARIABILITY.............................15
2.1.1 Spectral Information Measure (SIM)................................16
2.1.2 Hidden Markov Model(HMM)-Based Measure...................17
2.2 SPECTRAL SIMILARITY MEASURES.....................................20
2.2.1 Commonly Used Measures............................................20
2.2.1.1 Distance-Based Measures...................................20
2.2.1.2 Orthogonal Projection-Based Measures.................20
2.2.2 Spectral Information Divergence (SID).............................21
2.2.3 Hidden Markov Model-Based Information Divergence
(HMMID)...................................................................23
2.3 MEASURES OF SPECTRAL DISCRIMINABILITY....................23
2.3.1 Relative Spectral Discriminatory ProBability (RSDPB)..... 24
2.3.2 Relative Spectral Discriminatory PoWer(RSDPW)............24
2.3.3 Relative Spectral Discriminatory Entropy (RSDE)..............25
2.4 EXPERIMENTS......................................................................26
2.4.1 AVIRISData...............................................................26
HYPERSPECTRAL IMAGING
2.4.2 HYDICEData.............................................................31
2.5 CONCLUSIONS.....................................................................34
PART
II: SUBPIXEL DETECTION..........................................................37
3 TARGET ABUNDANCE-CONSTRAINED SUBPDŒL DETECTION:
PARTIALLY CONSTRAINED LEAST-SQUARES METHODS............39
3.1 INTRODUCTION....................................................................39
3.2 LINEAR SPECTRAL MIXTURE MODEL..................................40
3.3 ORTHOGONAL SUBSPACE PROJECTION (OSP).....................41
3.4 SUM-TO-ONE CONSTRAINED LEAST SQUARES METHOD
(SCLS)..................................................................................44
3.5 NONNEGATIVITY CONSTRAINED LEAST SQUARES METHOD
(NCLS)..................................................................................45
3.6 HYPERSPECTRAL IMAGE EXPERIMENTS.............................48
3.7 CONCLUSIONS.....................................................................50
4 TARGET SIGNATURE-CONSTRAINED SUBPIXEL DETECTION:
LINEARLY CONSTRAINED MINIMUM VARIANCE (LCMV)............51
4.1 INTRODUCTION....................................................................51
4.2 LCMV TARGET DETECTOR...................................................53
4.2.1 Constrained Energy Minimization (CEM).........................54
4.2.2 Target-Constrained Interference-Minimized Filter (TCIMF).. 55
4.3 RELATIONSHIP AMONG OSP, CEM AND TCIMF....................56
4.4 A COMPARARTIVE ANALYSIS BETWEEN CEM AND TCIMF. 58
4.4.1 Computer Simulations..................................................58
4.4.2 Hyperspectral Image Experiments....................................61
4.5 SENSITIVITY OF CEM AND TCIMF TO LEVEL OF TARGET
INFORMATION......................................................................63
4.5.1 Computer Simulations..................................................64
4.5.2 Hyperspectral Image Experiments....................................67
4.6 REAL-TIME PROCESSING.....................................................68
4.7 CONCLUSIONS................................................... 71
5 AUTOMATIC SUBPIXEL DETECTION: UNSUPERVISED SUBPIXEL
DETECTION...................................... -,-,
CONTENTS ri
5.1 INTRODUCTION....................................................................73
5.2 UNSUPERVISED VECTOR QUANTIZATION (UVQ) -BASED
ALGORITHM.........................................................................74
5.3 UNSUPERVISED TARGET GENERATION PROCESS (UTGP).... 75
5.4 UNSUPERVISED NCLS (UNCLS) ALGORITHM.......................78
5.5 EXPERIMENTS......................................................................80
5.6 CONCLUSIONS.....................................................................87
6 AUTOMATIC SUBPIXEL DETECTION: ANOMALY DETECTION.... 89
6.1 INTRODUCTION....................................................................89
6.2 RXD......................................................................................91
6.3 LPTDANDUTD.....................................................................94
6.4 RELATIONSHIP BETWEEN CEM AND RXD............................97
6.5 REAL-TIME PROCESSING.....................................................99
6.6 CONCLUSIONS...................................................................102
7 SENSITIVITY OF SUBPIXEL DETECTION....................................105
7.1 INTRODUCTION..................................................................105
7.2 SENSITIVITY OF TARGET KNOWLEDGE..............................107
7.3 SENSITIVITY OF NOISE.......................................................Ill
7.3.1 TSCSD....................................................................Ill
7.3.2 Hyperspectral Image Experiments..............................116
7.3.2.1 AVIRIS Data................................................116
7.3.2.2 HYDICEData.............................................125
7.4 SENSITIVITY OF ANOMALY DETECTION............................129
7.5 CONCLUSIONS...................................................................137
PART III: UNCONSTRAINED MIXED PIXEL CLASSIFICATION..........139
8 UNCONSTRAINED MIXED PIXEL CLASSIFICATION: LEAST-
SQUARES SUBSPACE PROJECTION.............................................141
8.1 INTRODUCTION..................................................................141
8.2 A POSTERIORI OSP..............................................................144
8.2.1 Signature Subspace Projection (SSP) Classifier...............144
HYPERSPECTRAL IMAGING
8.2.2 Target Subspace Projection (TSP) Classifier....................146
8.2.3 Oblique Subspace Projection (OBSP) Classifier...............147
8.2.4 Unconstrained Maximum Likelihood Estimation Classifier 148
8.3 ESTIMATION ERROR EVALUATED BY ROC ANALYSIS.......150
8.3.1 Signature Subspace Projection (SSP) Classifier...............151
8.3.2 Oblique Subspace Projection (OBSP) Classifier...............153
8.4 COMPUTER SIMULATIONS AND HYPERSPECTRAL IMAGE
EXPERIMENTS....................................................................153
8.4.1 Computer Simulations................................................154
8.4.2 Hyperspectral Data......................................................156
8.5 CONCLUSIONS...................................................................159
9 A QUANTITATIVE ANALYSIS OF MIXED-TO-PURE PIXEL
CONVERSION (MPCV)...................................................................161
9.1 INTRODUCTION..................................................................162
9.2 CONVERSION OF MPC TO PPC...........................................162
9.2.1 Mixed-to-Pure Pixel Converter (MPCV).........................163
9.2.2 Minimum Distance-Based Classification........................164
9.2.3 Fisher s Linear Discriminant Analysis (LDA)..................166
9.2.4 Unsupervised Classification.........................................169
9.3 CRITERIA FOR TARGET DETECTION AND
CLASSIFICATION................................................................169
9.4 COMPARATIVE PERFORMANCE ANALYSIS.......................171
9.5 CONCLUSIONS...................................................................177
PART IV: CONSTRAINED MIXED PIXEL CLASSIFICATION...............179
10 TARGET ABUNDANCE-CONSTRAINED MIXED PIXEL
CLASSIFICATION (TACMPC)........................................................181
10.1 INTRODUCTION..................................................................181
10.2 FULLY CONSTRAINED LEAST-SQUARES APPROACH.........183
10.2.1 Fully Constrained Least-Squares Method (FCLS)............183
10.2.2 Unsupervised FCLS Method (UFCLS)..........................183
10.3 MODIFIED FULLY CONSTRAINED LEAST-SQUARES (MFCLS)
APPROACH................................................./_ ,84
10.4 COMPUTER SIMULATIONS AND REAL HYPERSPECTRAL
IMAGE EXPERIMENTS........................................... 186
10.4.1 Computer Simulations........................ . . . . . . . . ................186
CONTENTS xiü
10.4.2 AVIRIS Image Experiments.................................................188
10.4.3 HYDICE Image Experiments...............................................193
10.5 NEAR REAL-TIME IMPLEMENTATION......................................201
10.6 CONCLUSIONS................................................................................205
11 TARGET SIGNATURE-CONSTRAINED MIXED PIXEL
CLASSIFICATION (TSCMPC): LCMV CLASSIFIERS..........................207
11.1 INTRODUCTION..............................................................................207
11.2 LCMV CLASSIFER...........................................................................208
11.3 BOWLES ET AL. S FILTER VECTORS (FV) ALGORITHM........209
11.4 COLOR ASSIGNMENT OF LCMV CLASSIFIERS........................211
11.5 EXTENSION OF CEM FILTER TO CLASSIFIERS........................213
11.5.1 Winner-Take-All CEM (WTACEM) Classifier...................213
11.5.2 Sum CEM (SCEM) Classifier..............................................213
11.5.3 Multiple-Target CEM (MTCEM) Classifier........................213
11.5.4 Target-Constrained Interference-Minimized (TCIM)
Classifier...............................................................................214
11.6 COMPUTER SIMULATIONS...........................................................214
11.7 HYPERSPECTRAL IMAGE EXPERIMENTS.................................218
11.8 REAL-TIME IMPLEMENTATION FOR LCMV CLASSIFIERS.... 223
11.9 CONCLUSIONS................................................................................227
12 TARGET SIGNATURE-CONSTRAINED MIXED PIXEL
CLASSIFICATION (TSCMPC): LINEARLY CONSTRAINED
DISCRIMINANT ANALYSIS (LCDA)........................................................229
12.1 INTRODUCTION..............................................................................229
12.2 LCDA.................................................................................................230
12.3 WHITENING PROCESS FOR LCDA...............................................233
12.4 BOWLES ET AL. S FILTER VECTORS (FV) ALGORITHM........234
12.5 COMPUTER SIMULATIONS AND HYPERSPECTRAL IMAGE
EXPERIMENTS.................................................................................235
12.6 CONCLUSIONS................................................................................240
PART V: AUTOMATIC MIXED PIXEL CLASSIFICATION (AMPC)------243
HYPERSPECTRAL IMAGING
xiv
13 AUTOMATIC MIXED PIXEL CLASSIFICATION (AMPC):
UNSUPERVISED MIXED PIXEL CLASSIFICATION.......................245
13.1 INTRODUCTION..................................................................245
13.2 UNSUPERVISED MPC.........................................................246
13.3 DESIRED TARGET DETECTION AND CLASSIFICATION.......246
13.4 AUTOMATIC TARGET DETECTION AND CLASSIFICATION.. 253
13.5 CONCLUSIONS...................................................................255
14 AUTOMATIC MIXED PIXEL CLASSIFICATION (AMPC): ANOMALY
CLASSIFICATION..........................................................................257
14.1 INTRODUCTION..................................................................257
14.2 TARGET DISCRIMINATION MEASURES..............................258
14.3 ANOMALY CLASSIFICATION..............................................260
14.4 AUTOMATIC THRESHOLDING METHOD..............................260
14.5 ANALYSIS ON TARGET CORRELATION USING TARGET
DISCRIMINATION MEASURES............................................265
14.6 ON-LINE IMPLEMENTATION...............................................270
14.7 CONCLUSIONS...................................................................274
15 AUTOMATIC MIXED PIXEL CLASSIFICATION (AMPC): LINEAR
SPECTRAL RANDOM MIXTURE ANALYSIS (LSRMA)...................277
15.1 INTRODUCTION..................................................................277
15.2 INDEPENDENT COMPONENT ANALYSIS (ICA)....................279
15.3 ICA-BASED LSRMA............................................................280
15.3.1 Relative Entropy-Based Measure for ICA........................281
15.3.2 Learning Algorithm to Find Separating Matrix W............282
15.4 EXPERIMENTS....................................................................284
15.4.1 A VIRIS Image Experiments..................... 284
15.4.2 HYDICE Image Experiments........................................289
15.5 3-D ROC ANALYSIS FOR LSRMA........................................295
15.6 CONCLUSIONS....................................................... 302
16 AUTOMATIC MIXED PIXEL CLASSIFICATION (AMPCV
PROJECTION PURSUIT...................................... _ 305
CONTENTS xv
16.1 INTRODUCTION..................................................................305
16.2 PROJECTION PURSUIT........................................................307
16.3 EVOLUTIONARY ALGORITHM (EA)....................................308
16.4 THRESHOLDING OF PROJECTION IMAGES USING ZERO-
DETECTION........................................................................310
16.5 EXPERIMENTS....................................................................311
16.5.1 A VIRIS Data Experiments...........................................311
16.5.2 HYDICE Data Experiments..........................................313
16.6 CONCLUSIONS...................................................................318
17 ESTIMATION FOR VIRTUAL DIMENSIONALITY OF
HYPERSPECTRAL IMAGERY........................................................319
17.1 INTRODUCTION..................................................................319
17.2 NEYMAN-PEARSON DETECTION THEORY-BASED EIGEN-
THRESHOLDING ANALYSIS (HFC METHOD).......................321
17.3 ESTIMATION OF NOISE CO VARIANCE MATRIX..................324
17.3.1 Residual Analysis (Roger, 1996)...................................325
17.3.2 Inter/Intra-Band Prediction Noise Estimation: Spatial/Spectral
Prediction Noise Estimation (Roger and Arnold, 1996).....326
17.4 NOISE ESTIMATION-BASED EIGEN-THRESHOLDING...........327
17.4.1 Noise-Whitened HFC (NWHFC) Method.......................327
17.4.2 Noise Subspace Projection (NSP)..................................327
17.5 OTHER METHODS FOR FINDING VD...................................329
17.5.1 Information Theoretic Criteria, AIC and MDL.................329
17.5.2 Malinowski s Empirical Indication Function (EIF) Method 330
17.6 COMPUTER SIMULATIONS AND HYPERSPECTRAL IMAGE
EXPERIMENTS....................................................................330
17.6.1 Computer Simulations................................................330
17.6.2 AVIRIS and HYDICE Image Experiments.....................333
17.7 CONCLUSIONS...................................................................336
18 CONCLUSIONS AND FURTHER TECHNIQUES.............................339
18.1 FUNCTIONAL TAXONOMY OF TECHNIQUES.......................339
18.2 MATHEMATICAL TAXONOMY OF TECHNIQUES.................341
18.3 EXPERIMENTS....................................................................343
ivj HYPERSPECTRAL IMAGING
18.4 ROC ANALYSIS FOR SUBPIXEL DETECTION AND MIXED
PIXEL CLASSIFICATION.....................................................344
18.5 SENSITIVITY ISSUES..........................................................345
18.5.1 Sensitivity to Level of Target Information......................345
18.5.2 Sensitivity to Noise....................................................345
18.6 REAL-TIME IMPLEMENTATION...........................................345
18.7 FURTHER TECHNIQUES......................................................346
18.7.1 Generalized Orthogonal Subspace Projection...................346
18.7.2 Convex Cone Analysis................................................347
18.7.3 Kaiman Filter-Based Linear Unmixing...........................348
18.7.4 Interference-Annihilated Eigen-Analysis..........................348
18.7.5 Band Selection..........................................................349
18.7.6 Linear Mixture Analysis-Based Data Compression...........350
18.7.7 Radial Basis Function Neural Network Approach.............351
18.8 APPLICATIONS TO MAGNETIC RESONANCE IMAGING.......351
GLOSSARY..........................................................................................353
REFERENCES......................................................................................357
INDEX..................................................................................................369
|
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| id | DE-604.BV014832015 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T11:08:38Z |
| institution | BVB |
| isbn | 0306474832 |
| language | English |
| lccn | 2002040796 |
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| publisher | Kluwer Academic/Plenum Publishers |
| record_format | marc |
| spellingShingle | Chang, Chein-I Hyperspectral imaging techniques for spectral detection and classification Photographie multibande Traitement d'images - Techniques numériques Télédétection Image processing Digital techniques Multispectral photography Remote sensing Fernerkundung (DE-588)4016796-3 gnd Multispektraltechnik (DE-588)4315379-3 gnd Bildverarbeitung (DE-588)4006684-8 gnd |
| subject_GND | (DE-588)4016796-3 (DE-588)4315379-3 (DE-588)4006684-8 |
| title | Hyperspectral imaging techniques for spectral detection and classification |
| title_auth | Hyperspectral imaging techniques for spectral detection and classification |
| title_exact_search | Hyperspectral imaging techniques for spectral detection and classification |
| title_full | Hyperspectral imaging techniques for spectral detection and classification Chein-I Chang |
| title_fullStr | Hyperspectral imaging techniques for spectral detection and classification Chein-I Chang |
| title_full_unstemmed | Hyperspectral imaging techniques for spectral detection and classification Chein-I Chang |
| title_short | Hyperspectral imaging |
| title_sort | hyperspectral imaging techniques for spectral detection and classification |
| title_sub | techniques for spectral detection and classification |
| topic | Photographie multibande Traitement d'images - Techniques numériques Télédétection Image processing Digital techniques Multispectral photography Remote sensing Fernerkundung (DE-588)4016796-3 gnd Multispektraltechnik (DE-588)4315379-3 gnd Bildverarbeitung (DE-588)4006684-8 gnd |
| topic_facet | Photographie multibande Traitement d'images - Techniques numériques Télédétection Image processing Digital techniques Multispectral photography Remote sensing Fernerkundung Multispektraltechnik Bildverarbeitung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=010036394&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT changcheini hyperspectralimagingtechniquesforspectraldetectionandclassification |
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Teilbibliothek Stammgelände
| Signatur: |
0002 BAU 967f 2006 A 7275 Lageplan |
|---|---|
| Exemplar 1 | Ausleihbar Am Standort |
Teilbibliothek Mathematik & Informatik
| Signatur: |
0102 BAU 967f 2006 A 3311 Lageplan |
|---|---|
| Exemplar 1 | Ausleihbar Am Standort |