Remote sensing digital image analysis: an introduction
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2013
|
| Ausgabe: | 5. ed. |
| Schlagwörter: | |
| Links: | http://deposit.dnb.de/cgi-bin/dokserv?id=4012494&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=025192702&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XIX, 494 S. Ill. graph. Darst. |
| ISBN: | 3642300618 9783642300615 |
Internformat
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| 100 | 1 | |a Richards, John Alan |d 1945- |e Verfasser |0 (DE-588)120671549 |4 aut | |
| 245 | 1 | 0 | |a Remote sensing digital image analysis |b an introduction |c John A. Richards |
| 250 | |a 5. ed. | ||
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2013 | |
| 300 | |a XIX, 494 S. |b Ill. graph. Darst. | ||
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| adam_text | IMAGE 1
CONTENTS
1 SOURCES AND CHARACTERISTICS O F REMOTE SENSING IMAGE DATA 1
1.1 ENERGY SOURCES AND WAVELENGTH RANGES 1
1.2 PRIMARY DATA CHARACTERISTICS 4
1.3 REMOTE SENSING PLATFORMS 6
1.4 WHAT EARTH SURFACE PROPERTIES ARE MEASURED? 10
1.4.1 SENSING IN THE VISIBLE AND REFLECTED INFRARED RANGES 12
1.4.2 SENSING IN THE THERMAL INFRARED RANGE 13
1.4.3 SENSING IN THE MICROWAVE RANGE 15
1.5 SPATIAL DATA SOURCES IN GENERAL AND GEOGRAPHIC INFORMATION SYSTEMS
18
1.6 SCALE IN DIGITAL IMAGE DATA 19
1.7 DIGITAL EARTH 20
1.8 HOW THIS BOOK IS ARRANGED 21
1.9 BIBLIOGRAPHY ON SOURCES AND CHARACTERISTICS OF REMOTE SENSING IMAGE
DATA 23
1.10 PROBLEMS 25
2 CORRECTING AND REGISTERING IMAGES 27
2.1 INTRODUCTION 27
2.2 SOURCES O F RADIOMETRIC DISTORTION 28
2.3 INSTRUMENTATION ERRORS 28
2.3.1 SOURCES O F DISTORTION 28
2.3.2 CORRECTING INSTRUMENTATION ERRORS 30
2.4 EFFECT OF THE SOLAR RADIATION CURVE AND THE ATMOSPHERE ON RADIOMETRY
31
2.5 COMPENSATING FOR THE SOLAR RADIATION CURVE 32
2.6 INFLUENCE OF THE ATMOSPHERE 33
2.7 EFFECT OF THE ATMOSPHERE ON REMOTE SENSING IMAGERY 37
IX
HTTP://D-NB.INFO/1021583464
IMAGE 2
X CONTENTS
2.8 CORRECTING ATMOSPHERIC EFFECTS IN BROAD
WAVEBAND SYSTEMS 38
2.9 CORRECTING ATMOSPHERIC EFFECTS IN NARROW WAVEBAND SYSTEMS 4 0
2.10 EMPIRICAL, DATA DRIVEN METHODS FOR ATMOSPHERIC CORRECTION 44
2.10.1 HAZE REMOVAL BY DARK SUBTRACTION 44
2.10.2 THE FLAT FIELD METHOD 45
2.10.3 THE EMPIRICAL LINE METHOD 45
2.10.4 LOG RESIDUALS 46
2.11 SOURCES OF GEOMETRIC DISTORTION 47
2.12 THE EFFECT OF EARTH ROTATION 48
2.13 THE EFFECT O F VARIATIONS IN PLATFORM ALTITUDE, ATTITUDE AND
VELOCITY 50
2.14 THE EFFECT OF SENSOR FIELD OF VIEW: PANORAMIC DISTORTION 51
2.15 THE EFFECT OF EARTH CURVATURE 53
2.16 GEOMETRIC DISTORTION CAUSED BY INSTRUMENTATION CHARACTERISTICS 54
2.16.1 SENSOR SCAN NONLINEARITIES 55
2.16.2 FINITE SCAN TIME DISTORTION 55
2.16.3 ASPECT RATIO DISTORTION 55
2.17 CORRECTION OF GEOMETRIC DISTORTION 56
2.18 USE OF MAPPING FUNCTIONS FOR IMAGE CORRECTION 56
2.18.1 MAPPING POLYNOMIALS AND THE USE OF GROUND CONTROL POINTS 57
2.18.2 BUILDING A GEOMETRICALLY CORRECT IMAGE 58
2.18.3 RESAMPLING AND THE NEED FOR INTERPOLATION 59
2.18.4 THE CHOICE O F CONTROL POINTS 61
2.18.5 EXAMPLE OF REGISTRATION TO A MAP GRID 62
2.19 MATHEMATICAL REPRESENTATION AND CORRECTION OF GEOMETRIC DISTORTION
64
2.19.1 ASPECT RATIO CORRECTION 64
2.19.2 EARTH ROTATION SKEW CORRECTION 65
2.19.3 IMAGE ORIENTATION TO NORTH-SOUTH 66
2.19.4 CORRECTING PANORAMIC EFFECTS 66
2.19.5 COMBINING THE CORRECTIONS 66
2.20 IMAGE TO IMAGE REGISTRATION 67
2.20.1 REFINING THE LOCALISATION O F CONTROL POINTS 67
2.20.2 EXAMPLE OF IMAGE TO IMAGE REGISTRATION 69
2.21 OTHER IMAGE GEOMETRY OPERATIONS 71
2.21.1 IMAGE ROTATION 71
2.21.2 SCALE CHANGING AND ZOOMING 72
IMAGE 3
CONTENTS XI
2.22 BIBLIOGRAPHY ON CORRECTING AND REGISTERING IMAGES 72
2.23 PROBLEMS 73
3 INTERPRETING IMAGES 79
3.1 INTRODUCTION 79
3.2 PHOTOINTERPRETATION 79
3.2.1 FORMS OF IMAGERY FOR PHOTOINTERPRETATION 80
3.2.2 COMPUTER ENHANCEMENT OF IMAGERY FOR PHOTOINTERPRETATION 82
3.3 QUANTITATIVE ANALYSIS: FROM DATA TO LABELS 83
3.4 COMPARING QUANTITATIVE ANALYSIS AND PHOTOINTERPRETATION . . . 84 3.5
THE FUNDAMENTALS OF QUANTITATIVE ANALYSIS 86
3.5.1 PIXEL VECTORS AND SPECTRAL SPACE 86
3.5.2 LINEAR CLASSIFIERS 88
3.5.3 STATISTICAL CLASSIFIERS 90
3.6 SUB-CLASSES AND SPECTRAL CLASSES 92
3.7 UNSUPERVISED CLASSIFICATION 93
3.8 BIBLIOGRAPHY ON INTERPRETING IMAGES 94
3.9 PROBLEMS 95
4 RADIOMETRIC ENHANCEMENT O F IMAGES 99
4.1 INTRODUCTION 99
4.1.1 POINT OPERATIONS AND LOOK UP TABLES 99
4.1.2 SCALAR AND VECTOR IMAGES 99
4.2 THE IMAGE HISTOGRAM 100
4.3 CONTRAST MODIFICATION 100
4.3.1 HISTOGRAM MODIFICATION RULE 100
4.3.2 LINEAR CONTRAST MODIFICATION 102
4.3.3 SATURATING LINEAR CONTRAST ENHANCEMENT 102
4.3.4 AUTOMATIC CONTRAST ENHANCEMENT 103
4.3.5 LOGARITHMIC AND EXPONENTIAL CONTRAST ENHANCEMENT 105
4.3.6 PIECEWISE LINEAR CONTRAST MODIFICATION 105
4.4 HISTOGRAM EQUALISATION 106
4.4.1 USE O F THE CUMULATIVE HISTOGRAM 106
4.4.2 ANOMALIES IN HISTOGRAM EQUALISATION 112
4.5 HISTOGRAM MATCHING 114
4.5.1 PRINCIPLE 114
4.5.2 IMAGE TO IMAGE CONTRAST MATCHING 115
4.5.3 MATCHING TO A MATHEMATICAL REFERENCE 115
4.6 DENSITY SLICING 118
4.6.1 BLACK AND WHITE DENSITY SLICING 118
4.6.2 COLOUR DENSITY SLICING AND PSEUDOCOLOURING 119
IMAGE 4
XII CONTENTS
4.7 BIBLIOGRAPHY ON RADIOMETRIC ENHANCEMENT O F IMAGES 120
4.8 PROBLEMS 122
5 GEOMETRIC PROCESSING AND ENHANCEMENT: IMAGE DOMAIN TECHNIQUES 127
5.1 INTRODUCTION 127
5.2 NEIGHBOURHOOD OPERATIONS IN IMAGE FILTERING 127
5.3 IMAGE SMOOTHING 130
5.3.1 MEAN VALUE SMOOTHING 130
5.3.2 MEDIAN FILTERING 132
5.3.3 MODAL FILTERING 133
5.4 SHARPENING AND EDGE DETECTION 133
5.4.1 SPATIAL GRADIENT METHODS 134
5.4.1.1 THE ROBERTS OPERATOR 135
5.4.1.2 THE SOBEL OPERATOR 136
5.4.1.3 THE PREWITT OPERATOR 136
5.4.1.4 THE LAPLACIAN OPERATOR 137
5.4.2 SUBTRACTIVE SMOOTHING (UNSHARP MASKING) 138
5.5 EDGE DETECTION 139
5.6 LINE AND SPOT DETECTION 141
5.7 THINNING AND LINKING 142
5.8 GEOMETRIC PROCESSING AS A CONVOLUTION OPERATION 142
5.9 IMAGE DOMAIN TECHNIQUES COMPARED WITH USING THE FOURIER TRANSFORM
145
5.10 GEOMETRIC PROPERTIES O F IMAGES 146
5.10.1 MEASURING GEOMETRIC PROPERTIES 146
5.10.2 DESCRIBING TEXTURE 147
5.11 MORPHOLOGICAL ANALYSIS 150
5.11.1 EROSION 152
5.11.2 DILATION 153
5.11.3 OPENING AND CLOSING 154
5.11.4 BOUNDARY EXTRACTION 155
5.11.5 OTHER MORPHOLOGICAL OPERATIONS 156
5.12 SHAPE RECOGNITION 157
5.13 BIBLIOGRAPHY ON GEOMETRIC PROCESSING AND ENHANCEMENT: IMAGE DOMAIN
TECHNIQUES 157
5.14 PROBLEMS 158
6 SPECTRAL DOMAIN IMAGE TRANSFORMS 161
6.1 INTRODUCTION 161
6.2 IMAGE ARITHMETIC AND VEGETATION INDICES 162
6.3 THE PRINCIPAL COMPONENTS TRANSFORMATION 163
6.3.1 THE MEAN VECTOR AND THE COVARIANCE MATRIX 164
6.3.2 A ZERO CORRELATION, ROTATIONAL TRANSFORM 167
6.3.3 THE EFFECT OF AN ORIGIN SHIFT 173
IMAGE 5
CONTENTS XIII
6.3.4 EXAMPLE AND SOME PRACTICAL CONSIDERATIONS 173
6.3.5 APPLICATION O F PRINCIPAL COMPONENTS IN IMAGE ENHANCEMENT AND
DISPLAY 176
6.3.6 THE TAYLOR METHOD OF CONTRAST ENHANCEMENT 178
6.3.7 USE O F PRINCIPAL COMPONENTS FOR IMAGE COMPRESSION 181
6.3.8 THE PRINCIPAL COMPONENTS TRANSFORM IN CHANGE DETECTION
APPLICATIONS 182
6.3.9 USE O F PRINCIPAL COMPONENTS FOR FEATURE REDUCTION 186
6.4 THE NOISE ADJUSTED PRINCIPAL COMPONENTS TRANSFORM 186
6.5 THE KAUTH-THOMAS TASSELED CAP TRANSFORM 189
6.6 THE KERNEL PRINCIPAL COMPONENTS TRANSFORMATION 192
6.7 HSI IMAGE DISPLAY 195
6.8 PAN SHARPENING 197
6.9 BIBLIOGRAPHY ON SPECTRAL DOMAIN IMAGE TRANSFORMS 198
6.10 PROBLEMS 199
7 SPATIAL DOMAIN IMAGE TRANSFORMS 203
7.1 INTRODUCTION 203
7.2 SPECIAL FUNCTIONS 204
7.2.1 THE COMPLEX EXPONENTIAL FUNCTION 204
7.2.2 THE IMPULSE OR DELTA FUNCTION 206
7.2.3 THE HEAVISIDE STEP FUNCTION 207
7.3 THE FOURIER SERIES 207
7.4 THE FOURIER TRANSFORM 210
7.5 THE DISCRETE FOURIER TRANSFORM 212
7.5.1 PROPERTIES O F THE DISCRETE FOURIER TRANSFORM 214
7.5.2 COMPUTING THE DISCRETE FOURIER TRANSFORM 215
7.6 CONVOLUTION 215
7.6.1 THE CONVOLUTION INTEGRAL 215
7.6.2 CONVOLUTION WITH AN IMPULSE 216
7.6.3 THE CONVOLUTION THEOREM 216
7.6.4 DISCRETE CONVOLUTION 217
7.7 SAMPLING THEORY 218
7.8 THE DISCRETE FOURIER TRANSFORM OF AN IMAGE 221
7.8.1 THE TRANSFORMATION EQUATIONS 221
7.8.2 EVALUATING THE FOURIER TRANSFORM OF AN IMAGE 222
7.8.3 THE CONCEPT O F SPATIAL FREQUENCY 223
7.8.4 DISPLAYING THE DFT O F AN IMAGE 223
7.9 IMAGE PROCESSING USING THE FOURIER TRANSFORM 224
7.10 CONVOLUTION IN TWO DIMENSIONS 226
7.11 OTHER FOURIER TRANSFORMS 227
7.12 LEAKAGE AND WINDOW FUNCTIONS 227
IMAGE 6
XIV CONTENTS
7.13 THE WAVELET TRANSFORM 229
7.13.1 BACKGROUND 229
7.13.2 ORTHOGONAL FUNCTIONS AND INNER PRODUCTS 229
7.13.3 WAVELETS AS BASIS FUNCTIONS 230
7.13.4 DYADIC WAVELETS WITH COMPACT SUPPORT 232
7.13.5 CHOOSING THE WAVELETS 232
7.13.6 FILTER BANKS 233
7.13.6.1 SUB BAND FILTERING, AND DOWNSAMPLING . . . 233 7.13.6.2
RECONSTRUCTION FROM THE WAVELETS, AND UPSAMPLING 237
7.13.6.3 RELATIONSHIP BETWEEN THE LOW AND HIGH PASS FILTERS 238
7.13.7 CHOICE O F WAVELETS 239
7.14 THE WAVELET TRANSFORM O F AN IMAGE 241
7.15 APPLICATIONS O F THE WAVELET TRANSFORM IN REMOTE SENSING IMAGE
ANALYSIS 241
7.16 BIBLIOGRAPHY ON SPATIAL DOMAIN IMAGE TRANSFORMS 243
7.17 PROBLEMS 244
8 SUPERVISED CLASSIFICATION TECHNIQUES 247
8.1 INTRODUCTION 247
8.2 THE ESSENTIAL STEPS IN SUPERVISED CLASSIFICATION 248
8.3 MAXIMUM LIKELIHOOD CLASSIFICATION 250
8.3.1 BAYES CLASSIFICATION 250
8.3.2 THE MAXIMUM LIKELIHOOD DECISION RULE 251
8.3.3 MULTIVARIATE NORMAL CLASS MODELS 252
8.3.4 DECISION SURFACES 253
8.3.5 THRESHOLDS 253
8.3.6 NUMBER O F TRAINING PIXELS REQUIRED 255
8.3.7 THE HUGHES PHENOMENON AND THE CURSE OF DIMENSIONALITY 256
8.3.8 AN EXAMPLE 258
8.4 GAUSSIAN MIXTURE MODELS 260
8.5 MINIMUM DISTANCE CLASSIFICATION 265
8.5.1 THE CASE OF LIMITED TRAINING DATA 265
8.5.2 THE DISCRIMINANT FUNCTION 267
8.5.3 DECISION SURFACES FOR THE MINIMUM DISTANCE CLASSIFIER 267
8.5.4 THRESHOLDS 268
8.5.5 DEGENERATION O F MAXIMUM LIKELIHOOD TO MINIMUM DISTANCE
CLASSIFICATION 268
8.5.6 CLASSIFICATION TIME COMPARISON O F THE MAXIMUM LIKELIHOOD AND
MINIMUM DISTANCE RULES 269
8.6 PARALLELEPIPED CLASSIFICATION 269
IMAGE 7
CONTENTS X V
8.7 MAHALANOBIS CLASSIFICATION 271
8.8 NON-PARAMETRIC CLASSIFICATION 271
8.9 TABLE LOOK U P CLASSIFICATION 272
8.10 NN (NEAREST NEIGHBOUR) CLASSIFICATION 273
8.11 THE SPECTRAL ANGLE MAPPER 274
8.12 NON-PARAMETRIC CLASSIFICATION FROM A GEOMETRIC BASIS 274
8.12.1 THE CONCEPT OF A WEIGHT VECTOR 274
8.12.2 TESTING CLASS MEMBERSHIP 275
8.13 TRAINING A LINEAR CLASSIFIER 276
8.14 THE SUPPORT VECTOR MACHINE: LINEARLY SEPARABLE CLASSES . . . 276
8.15 THE SUPPORT VECTOR MACHINE: OVERLAPPING CLASSES 281
8.16 THE SUPPORT VECTOR MACHINE: NONLINEARLY SEPARABLE DATA AND KERNELS
283
8.17 MULTI-CATEGORY CLASSIFICATION WITH BINARY CLASSIFIERS 286
8.18 COMMITTEES OF CLASSIFIERS 288
8.18.1 BAGGING 288
8.18.2 BOOSTING AND ADABOOST 289
8.19 NETWORKS OF CLASSIFIERS: THE NEURAL NETWORK 290
8.19.1 THE PROCESSING ELEMENT 291
8.19.2 TRAINING THE NEURAL NETWORK-BACKPROPAGATION. . . . 292 8.19.3
CHOOSING THE NETWORK PARAMETERS 296
8.19.4 EXAMPLE 297
8.20 CONTEXT CLASSIFICATION 299
8.20.1 THE CONCEPT O F SPATIAL CONTEXT 299
8.20.2 CONTEXT CLASSIFICATION BY IMAGE PRE-PROCESSING . . . . 302 8.20.3
POST CLASSIFICATION FILTERING 302
8.20.4 PROBABILISTIC RELAXATION LABELLING 303
8.20.4.1 THE ALGORITHM 303
8.20.4.2 THE NEIGHBOURHOOD FUNCTION 304
8.20.4.3 DETERMINING THE COMPATIBILITY COEFFICIENTS 305
8.20.4.4 STOPPING THE PROCESS 306
8.20.4.5 EXAMPLES 307
8.20.5 HANDLING SPATIAL CONTEXT BY MARKOV RANDOM FIELDS 308
8.21 BIBLIOGRAPHY ON SUPERVISED CLASSIFICATION TECHNIQUES 312
8.22 PROBLEMS 315
9 CLUSTERING AND UNSUPERVISED CLASSIFICATION 319
9.1 HOW CLUSTERING IS USED 319
9.2 SIMILARITY METRICS AND CLUSTERING CRITERIA 320
9.3 K MEANS CLUSTERING 322
9.3.1 THE K MEANS ALGORITHM 322
9.4 ISODATA CLUSTERING 323
IMAGE 8
XVI CONTENTS
9.4.1 MERGING AND DELETING CLUSTERS 323
9.4.2 SPLITTING ELONGATED CLUSTERS 325
9.5 CHOOSING THE INITIAL CLUSTER CENTRES 325
9.6 COST OF K MEANS AND ISODATA CLUSTERING 326
9.7 UNSUPERVISED CLASSIFICATION 326
9.8 AN EXAMPLE O F CLUSTERING WITH THE K MEANS ALGORITHM . . . . 327
9.9 A SINGLE PASS CLUSTERING TECHNIQUE 327
9.9.1 THE SINGLE PASS ALGORITHM 327
9.9.2 ADVANTAGES AND LIMITATIONS O F THE SINGLE PASS ALGORITHM 329
9.9.3 STRIP GENERATION PARAMETER 329
9.9.4 VARIATIONS ON THE SINGLE PASS ALGORITHM 330
9.9.5 AN EXAMPLE O F CLUSTERING WITH THE SINGLE PASS ALGORITHM 331
9.10 HIERARCHICAL CLUSTERING 331
9.10.1 AGGLOMERATIVE HIERARCHICAL CLUSTERING 332
9.11 OTHER CLUSTERING METRICS 333
9.12 OTHER CLUSTERING TECHNIQUES 333
9.13 CLUSTER SPACE CLASSIFICATION 335
9.14 BIBLIOGRAPHY ON CLUSTERING AND UNSUPERVISED CLASSIFICATION 339
9.15 PROBLEMS 340
10 FEATURE REDUCTION 343
10.1 THE NEED FOR FEATURE REDUCTION 343
10.2 A NOTE ON HIGH DIMENSIONAL DATA 344
10.3 MEASURES OF SEPARABILITY 345
10.4 DIVERGENCE 345
10.4.1 DEFINITION 345
10.4.2 DIVERGENCE O F A PAIR OF NORMAL DISTRIBUTIONS 347
10.4.3 USING DIVERGENCE FOR FEATURE SELECTION 348
10.4.4 A PROBLEM WITH DIVERGENCE 349
10.5 THE JEFFRIES-MATUSITA (JM) DISTANCE 350
10.5.1 DEFINITION 350
10.5.2 COMPARISON OF DIVERGENCE AND JM DISTANCE 351
10.6 TRANSFORMED DIVERGENCE 351
10.6.1 DEFINITION 351
10.6.2 TRANSFORMED DIVERGENCE AND THE PROBABILITY OF CORRECT
CLASSIFICATION 352
10.6.3 USE OF TRANSFORMED DIVERGENCE IN CLUSTERING 353
10.7 SEPARABILITY MEASURES FOR MINIMUM DISTANCE CLASSIFICATION 353
10.8 FEATURE REDUCTION BY SPECTRAL TRANSFORMATION 354
IMAGE 9
CONTENTS
X V N
10.8.1 FEATURE REDUCTION USING THE PRINCIPAL COMPONENTS
TRANSFORMATION 354
10.8.2 FEATURE REDUCTION USING THE CANONICAL ANALYSIS TRANSFORMATION 356
10.8.2.1 WITHIN CLASS AND AMONG CLASS COVARIANCE 358
10.8.2.2 A SEPARABILITY MEASURE 359
10.8.2.3 THE GENERALISED EIGENVALUE EQUATION. . . . 359 10.8.2.4 AN
EXAMPLE 360
10.8.3 DISCRIMINANT ANALYSIS FEATURE EXTRACTION (DAFE) . . . 362 10.8.4
NON-PARAMETRIC DISCRIMINANT ANALYSIS (NDA) 364
10.8.5 DECISION BOUNDARY FEATURE EXTRACTION (DBFE) . . . . 368 10.8.6
NON-PARAMETRIC WEIGHTED FEATURE EXTRACTION (NWFE) 369
10.9 BLOCK DIAGONALISING THE COVARIANCE MATRIX 370
10.10 IMPROVING COVARIANCE ESTIMATES THROUGH REGULARISATION . . . 375
10.11 BIBLIOGRAPHY ON FEATURE REDUCTION 377
10.12 PROBLEMS 378
11 IMAGE CLASSIFICATION IN PRACTICE 381
11.1 INTRODUCTION 381
11.2 AN OVERVIEW OF CLASSIFICATION 382
11.2.1 PARAMETRIC AND NON-PARAMETRIC SUPERVISED CLASSIFIERS 382
11.2.2 UNSUPERVISED CLASSIFICATION 383
11.2.3 SEMI-SUPERVISED CLASSIFICATION 384
11.3 SUPERVISED CLASSIFICATION WITH THE MAXIMUM LIKELIHOOD RULE 384
11.3.1 OUTLINE 384
11.3.2 GATHERING TRAINING DATA 385
11.3.3 FEATURE SELECTION 386
11.3.4 RESOLVING MULTIMODAL DISTRIBUTIONS 387
11.3.5 EFFECT O F RESAMPLING ON CLASSIFICATION 387
11.4 A HYBRID SUPERVISED/UNSUPERVISED METHODOLOGY 388
11.4.1 OUTLINE O F THE METHOD 388
11.4.2 CHOOSING THE IMAGE SEGMENTS TO CLUSTER 389
11.4.3 RATIONALISING THE NUMBER OF SPECTRAL CLASSES 390
11.4.4 AN EXAMPLE 390
11.5 CLUSTER SPACE CLASSIFICATION 393
11.6 SUPERVISED CLASSIFICATION USING THE SUPPORT VECTOR MACHINE 394
11.6.1 INITIAL CHOICES 394
11.6.2 GRID SEARCHING FOR PARAMETER DETERMINATION 395
11.6.3 DATA CENTERING AND SCALING 395
IMAGE 10
XV111
CONTENTS
11.7 ASSESSING CLASSIFICATION ACCURACY 396
11.7.1 USE O F A TESTING SET O F PIXELS 396
11.7.2 THE ERROR MATRIX 397
11.7.3 QUANTIFYING THE ERROR MATRIX 398
11.7.4 THE KAPPA COEFFICIENT 401
11.7.5 NUMBER O F TESTING SAMPLES REQUIRED FOR ASSESSING MAP ACCURACY
405
11.7.6 NUMBER O F TESTING SAMPLES REQUIRED FOR POPULATING THE ERROR
MATRIX 410
11.7.7 PLACING CONFIDENCE LIMITS ON ASSESSED ACCURACY. . . 412 11.7.8
CROSS VALIDATION ACCURACY ASSESSMENT AND THE LEAVE ONE OUT METHOD 413
11.8 DECISION TREE CLASSIFIERS 413
11.8.1 CART (CLASSIFICATION AND REGRESSION TREES) 415
11.8.2 RANDOM FORESTS 420
11.8.3 PROGRESSIVE TWO-CLASS DECISION CLASSIFIER 421
11.9 IMAGE INTERPRETATION THROUGH SPECTROSCOPY AND SPECTRAL LIBRARY
SEARCHING 422
11.10 END MEMBERS AND UNMIXING 424
11.11 IS THERE A BEST CLASSIFIER? 426
11.12 BIBLIOGRAPHY ON IMAGE CLASSIFICATION IN PRACTICE 431
11.13 PROBLEMS 433
12 MULTISOURCE IMAGE ANALYSIS 437
12.1 INTRODUCTION 437
12.2 STACKED VECTOR ANALYSIS 438
12.3 STATISTICAL MULTISOURCE METHODS 439
12.3.1 JOINT STATISTICAL DECISION RULES 439
12.3.2 COMMITTEE CLASSIFIERS 441
12.3.3 OPINION POOLS AND CONSENSUS THEORY 442
12.3.4 USE O F PRIOR PROBABILITIES 443
12.3.5 SUPERVISED LABEL RELAXATION 443
12.4 THE THEORY OF EVIDENCE 444
12.4.1 THE CONCEPT OF EVIDENTIAL MASS 444
12.4.2 COMBINING EVIDENCE WITH THE ORTHOGONAL SUM . . . . 446
12.4.3 DECISION RULES 448
12.5 KNOWLEDGE-BASED IMAGE ANALYSIS 448
12.5.1 EMULATING PHOTOINTERPRETATION TO UNDERSTAND KNOWLEDGE PROCESSING
449
12.5.2 THE STRUCTURE OF A KNOWLEDGE-BASED IMAGE ANALYSIS SYSTEM 450
12.5.3 REPRESENTING KNOWLEDGE IN A KNOWLEDGE-BASED IMAGE ANALYSIS SYSTEM
452
12.5.4 PROCESSING KNOWLEDGE: THE INFERENCE ENGINE 454
IMAGE 11
CONTENTS XIX
12.5.5 RULES AS JUSTIFIERS OF A LABELLING PROPOSITION 454
12.5.6 ENDORSING A LABELLING PROPOSITION 455
12.5.7 AN EXAMPLE 456
12.6 OPERATIONAL MULTISOURCE ANALYSIS 458
12.7 BIBLIOGRAPHY ON MULTISOURCE IMAGE ANALYSIS 461
12.8 PROBLEMS 463
APPENDIX A: SATELLITE ALTITUDES AND PERIODS 465
APPENDIX B: BINARY REPRESENTATION O F DECIMAL NUMBERS 467
APPENDIX C: ESSENTIAL RESULTS FROM VECTOR AND MATRIX ALGEBRA 469
APPENDIX D: SOME FUNDAMENTAL MATERIAL FROM PROBABILITY AND STATISTICS
479
APPENDIX E: PENALTY FUNCTION DERIVATION O F THE MAXIMUM LIKELIHOOD
DECISION RULE 483
INDEX 487
|
| any_adam_object | 1 |
| author | Richards, John Alan 1945- |
| author_GND | (DE-588)120671549 |
| author_facet | Richards, John Alan 1945- |
| author_role | aut |
| author_sort | Richards, John Alan 1945- |
| author_variant | j a r ja jar |
| building | Verbundindex |
| bvnumber | BV040338398 |
| classification_rvk | RB 10232 ZI 9550 ZN 6050 |
| classification_tum | DAT 760f BAU 967f |
| ctrlnum | (OCoLC)802787269 (DE-599)DNB1021583464 |
| dewey-full | 621.3678 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
| dewey-raw | 621.3678 |
| dewey-search | 621.3678 |
| dewey-sort | 3621.3678 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Informatik Bauingenieurwesen Elektrotechnik / Elektronik / Nachrichtentechnik Geographie |
| edition | 5. ed. |
| format | Book |
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| genre_facet | Einführung |
| id | DE-604.BV040338398 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T16:12:52Z |
| institution | BVB |
| isbn | 3642300618 9783642300615 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-025192702 |
| oclc_num | 802787269 |
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| physical | XIX, 494 S. Ill. graph. Darst. |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | Springer |
| record_format | marc |
| spellingShingle | Richards, John Alan 1945- Remote sensing digital image analysis an introduction Datenverarbeitung (DE-588)4011152-0 gnd Fernerkundung (DE-588)4016796-3 gnd Bildverarbeitung (DE-588)4006684-8 gnd |
| subject_GND | (DE-588)4011152-0 (DE-588)4016796-3 (DE-588)4006684-8 (DE-588)4151278-9 |
| title | Remote sensing digital image analysis an introduction |
| title_auth | Remote sensing digital image analysis an introduction |
| title_exact_search | Remote sensing digital image analysis an introduction |
| title_full | Remote sensing digital image analysis an introduction John A. Richards |
| title_fullStr | Remote sensing digital image analysis an introduction John A. Richards |
| title_full_unstemmed | Remote sensing digital image analysis an introduction John A. Richards |
| title_short | Remote sensing digital image analysis |
| title_sort | remote sensing digital image analysis an introduction |
| title_sub | an introduction |
| topic | Datenverarbeitung (DE-588)4011152-0 gnd Fernerkundung (DE-588)4016796-3 gnd Bildverarbeitung (DE-588)4006684-8 gnd |
| topic_facet | Datenverarbeitung Fernerkundung Bildverarbeitung Einführung |
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| work_keys_str_mv | AT richardsjohnalan remotesensingdigitalimageanalysisanintroduction |
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Teilbibliothek Stammgelände
| Signatur: |
0002 BAU 967f 2008 A 3872(5) Lageplan |
|---|---|
| Exemplar 1 | Ausleihbar Am Standort |