Figures ...................................................... xvii
Tables
Preface to the Third Edition ............................... xxxvii
Preface to the Second Edition .............................. xxxvii
CHAPTER 1 The Nature of Remote Sensing .......................... 1
1.1 Introduction ............................................... 1
1.2 Remote Sensing ............................................. 2
1.2.1 Information Extraction from Remote-Sensing Images ... 7
1.2.2 Spectral Factors in Remote Sensing .................. 8
1.3 Spectral Signatures ....................................... 13
1.4 Remote-Sensing Systems .................................... 16
1.4.1 Spatial and Radiometric Characteristics ............ 16
1.4.2 Spectral Characteristics ........................... 30
1.4.3 Temporal Characteristics ........................... 32
1.4.4 Multi-Sensor Formation Flying ...................... 35
1.5 Image Display Systems ..................................... 36
1.6 Data Systems .............................................. 39
1.7 Summary ................................................... 42
1.8 Exercises ................................................. 44
CHAPTER 2 Optical Radiation Models ............................. 45
2.1 Introduction ............................................... 45
2.2 Visible to Shortwave Infrared Region ...................... 46
2.2.1 Solar Radiation .................................... 46
2.2.2 Radiation Components ............................... 47
Surface-reflected, unscattered component ........... 48
Surface-reflected, atmosphere-scattered
component .......................................... 53
Path-scattered component ........................... 54
Total at-sensor, solar radiance .................... 55
2.2.3 Image Examples in the Solar Region ................. 58
Terrain shading .................................... 58
Shadowing .......................................... 58
Atmospheric correction ............................. 61
2.3 Midwave to Thermal Infrared Region ........................ 61
2.3.1 Thermal Radiation .................................. 61
2.3.2 Radiation Components ............................... 63
Surface-emitted component .......................... 64
Surface-reflected, atmosphere-emitted component .... 66
Path-emitted component ............................. 67
Total at-sensor, emitted radiance .................. 68
2.3.3 Total Solar and Thermal Upwelling Radiance ......... 68
2.3.4 Image Examples in the Thermal Region ............... 69
2.4 Summary ................................................... 72
2.5 Exercises ................................................. 73
CHAPTER 3 Sensor Models ........................................ 75
3.1 Introduction .............................................. 75
3.2 Overall Sensor Model ...................................... 76
3.3 Resolution ................................................ 76
3.3.1 The Instrument Response ............................ 77
3.3.2 Spatial Resolution ................................. 77
3.3.3 Spectral Resolution ................................ 82
3.4 Spatial Response .......................................... 85
3.4.1 Optical PSFopt ...................................... 86
3.4.2 Detector PSFdet .................................... 88
3.4.3 Image Motion PSFIM ................................. 88
3.4.4 Electronics PSFel .................................. 90
3.4.5 Net PSFnet ......................................... 90
3.4.6 Comparison of Sensor PSFs .......................... 90
3.4.7 Imaging System Simulation .......................... 91
3.4.8 Measuring the PSF .................................. 95
ALI LSF measurement ................................ 98
QuickBird LSF measurement ......................... 101
3.5 Spectral Response ........................................ 104
3.6 Signal Amplification ..................................... 106
3.7 Sampling and Quantization ................................ 107
3.8 Simplified Sensor Model .................................. 109
3.9 Geometric Distortion ..................................... 110
3.9.1 Sensor Location Models ............................ 110
3.9.2 Sensor Attitude Models ............................ 110
3.9.3 Scanner Models .................................... 113
3.9.4 Earth Model ....................................... 114
3.9.5 Line and Whiskbroom Scan Geometry ................. 119
3.9.6 Pushbroom Scan Geometry ........................... 119
3.9.7 Topographic Distortion ............................ 121
3.10 Summary .................................................. 125
3.11 Exercises ................................................ 125
CHAPTER 4 Data Models ......................................... 727
4.1 Introduction ............................................. 127
4.2 A Word on Notation ....................................... 128
4.3 Univariate Image Statistics .............................. 128
4.3.1 Histogram ......................................... 129
Normal distribution ............................... 130
4.3.2 Cumulative Histogram .............................. 131
4.3.3 Statistical Parameters ............................ 131
4.4 Multivariate Image Statistics ............................ 133
4.4.1 Reduction to Univariate Statistics ................. 140
4.5 Noise Models ............................................. 140
4.5.1 Statistical Measures of Image Quality ............. 146
Contrast .......................................... 146
Modulation ........................................ 146
Signal-to-Noise Ratio (SNR) ....................... 147
National Imagery Interpretability Scale (NIIRS) ... 149
4.5.2 Noise Equivalent Signal ........................... 152
4.6 Spatial Statistics ....................................... 152
4.6.1 Visualization of Spatial Covariance ............... 153
4.6.2 Covariance and Semivariogram ...................... 153
Separability and anisotropy ....................... 160
4.6.3 Power Spectral Density ............................ 162
4.6.4 Co-Occurrence Matrix .............................. 164
4.6.5 Fractal Geometry .................................. 166
4.7 Topographic and Sensor Effects ........................... 169
4.7.1 Topography and Spectral Scattergrams .............. 169
4.7.2 Sensor Characteristics and Spatial Statistics ..... 174
4.7.3 Sensor Characteristics and Spectral
Scattergrams ...................................... 178
4.8 Summary .................................................. 181
4.9 Exercises ................................................ 182
CHAPTER 5 Spectral Transforms ................................. 183
5.1 Introduction ............................................. 183
5.2 Feature Space ............................................ 184
5.3 Multispectral Ratios ..................................... 186
5.3.1 Vegetation Indexes ................................ 188
5.3.2 Image Examples .................................... 191
5.4 Principal Components ..................................... 193
5.4.1 Standardized Principal Components (SPC) ........... 199
5.4.2 Maximum Noise Fraction (MNF) ...................... 199
5.5 Tasseled-Cap Components .................................. 202
5.6 Contrast Enhancement ..................................... 206
5.6.1 Global Transforms ................................. 208
Linear stretch .................................... 209
Nonlinear stretch ................................. 209
Normalization stretch ............................. 210
Reference stretch ................................. 210
Thresholding ...................................... 216
5.6.2 Local Transforms .................................. 217
5.6.3 Color Images ...................................... 219
Min-max stretch ................................... 221
Normalization stretch ............................. 221
Reference stretch ................................. 221
Decorrelation stretch ............................. 222
Color-space transforms ............................ 223
Spatial domain blending ........................... 224
5.7 Summary .................................................. 227
5.8 Exercises ................................................ 227
CHAPTER 6 Spatial Transforms .................................. 229
6.1 Introduction ............................................. 229
6.2 An Image Model for Spatial Filtering ..................... 230
6.3 Convolution Filters ...................................... 230
6.3.1 Linear Filters .................................... 232
Convolution ....................................... 232
Low-pass and high-pass filters (LPF, HPF) ........ 233
High-boost filters (HBF) .......................... 234
Band-pass filters (BPF) ........................... 235
Directional filters ............................... 236
The border region ................................. 237
Characteristics of filtered images ................ 239
Application of the blending algorithm to spatial
filtering ......................................... 239
The box-filter algorithm .......................... 240
Cascaded linear filters ........................... 241
6.3.2 Statistical Filters ............................... 242
Morphological filters ............................. 244
6.3.3 Gradient Filters .................................. 245
6.4 Fourier Transforms ....................................... 246
6.4.1 Fourier Analysis and Synthesis .................... 246
6.4.2 Discrete Fourier Transforms in 2-D ................ 249
6.4.3 The Fourier Components ............................ 253
6.4.4 Filtering with the Fourier Transform .............. 255
Transfer functions ................................ 257
6.4.5 System Modeling Using the Fourier Transform ....... 259
6.4.6 The Power Spectrum ................................ 263
6.5 Scale-Space Transforms ................................... 263
6.5.1 Image Resolution Pyramids ......................... 265
6.5.2 Zero-Crossing Filters ............................. 267
Laplacian-of-Gaussian (LoG) filters ............... 269
Difference-of-Gaussians (DoG) filters ............. 274
6.5.3 Wavelet Transforms ................................ 278
6.6 Summary .................................................. 282
6.7 Exercises ................................................ 282
CHAPTER 7 Correction and Calibration .......................... 285
7.1 Introduction ............................................. 285
7.2 Distortion Correction .................................... 286
7.2.1 Polynomial Distortion Models ...................... 287
Ground Control Points (GCPs) ...................... 291
7.2.2 Coordinate Transformation ......................... 298
Map projections ................................... 299
7.2.3 Resampling ........................................ 300
7.3 Sensor MTF Compensation .................................. 309
7.3.1 Examples of MTF compensation ....................... 311
7.4 Noise Reduction .......................................... 315
7.4.1 Global Noise ...................................... 315
Sigma filter ...................................... 315
Nagao-Matsuyama filter ............................ 317
7.4.2 Local Noise ....................................... 318
Detection by spectral correlation ................. 318
7.4.3 Periodic Noise .................................... 320
7.4.4 Detector Striping ................................. 323
Global, linear detector matching .................. 325
Nonlinear detector matching ....................... 325
Statistical modification .......................... 325
Spatial filter masking ............................ 327
Debanding ......................................... 328
7.5 Radiometric Calibration .................................. 332
7.5.1 Multispectral Sensors and Imagery ................. 334
Sensor calibration ................................ 334
Atmospheric correction ............................ 337
Solar and topographic correction .................. 339
Image examples .................................... 340
7.5.2 Hyperspectral Sensors and Imagery ................. 341
Sensor calibration ................................ 341
Atmospheric correction ............................ 343
Normalization techniques .......................... 344
Image examples .................................... 350
7.6 Summary .................................................. 352
7.7 Exercises ................................................ 353
CHAPTER 8 Registration and Fusion ............................. 355
8.1 Introduction ............................................. 355
8.2 What Is Registration? .................................... 356
8.3 Automated GCP Location ................................... 357
8.3.1 Area Correlation .................................. 357
Relation to spatial statistics .................... 362
8.3.2 Other Spatial Features for Registration ........... 362
8.4 Orthorectification ....................................... 363
8.4.1 Low-Resolution DEM ................................ 363
8.4.2 High-Resolution DEM ............................... 364
Hierarchical warp stereo .......................... 366
8.5 Multi-Image Fusion ....................................... 371
8.5.1 Feature Space Fusion .............................. 374
8.5.2 Spatial Domain Fusion ............................. 375
High frequency modulation ......................... 376
Filter design for HFM ............................. 378
Sharpening with a sensor model .................... 378
8.5.3 Scale-Space Fusion ................................ 380
8.5.4 Image Fusion Examples ............................. 380
8.6 Summary .................................................. 384
8.7 Exercises ................................................ 384
CHAPTER 9 Thematic Classification ............................. 387
9.1 Introduction ............................................. 387
9.2 The Classification Process ............................... 388
9.2.1 The Importance of Image Scale and Resolution ...... 390
9.2.2 The Notion of Similarity .......................... 391
9.2.3 Hard Versus Soft Classification ................... 393
9.3 Feature Extraction ....................................... 395
9.4 Training the Classifier .................................. 395
9.4.1 Supervised Training ............................... 396
Separability analysis ............................. 396
9.4.2 Unsupervised Training ............................. 399
K-means clustering algorithm ...................... 400
Clustering examples ............................... 400
9.4.3 Hybrid Supervised/Unsupervised Training ........... 402
9.5 Nonparametric Classification ............................. 405
9.5.1 Level-Slice Classifier ............................ 405
9.5.2 Histogram Estimation Classifier ................... 406
9.5.3 Nearest-Neighbors Classifier ...................... 407
9.5.4 Artificial Neural Network (ANN) Classifier ........ 407
Back-propagation algorithm ........................ 409
9.5.5 Nonparametric Classification Examples ............. 413
9.6 Parametric Classification ................................ 417
9.6.1 Estimation of Model Parameters .................... 417
9.6.2 Discriminant Functions ............................ 418
9.6.3 The Normal Distribution Model ..................... 418
9.6.4 The Nearest-Mean Classifier ....................... 421
9.6.5 Parametric Classification Examples ................ 422
9.7 Spatial-Spectral Segmentation ............................ 427
9.7.1 Region Growing ..................................... 427
9.8 Subpixel Classification .................................. 430
9.8.1 The Linear Mixing Model ........................... 434
Unmixing examples ................................. 437
Relation of fractions to neural network output .... 440
Endmember specification ........................... 441
9.8.2 Fuzzy Set Classification .......................... 442
Fuzzy C-Means (FCM) clustering .................... 442
Fuzzy supervised classification ................... 443
9.9 Hyperspectral Image Analysis ............................. 445
9.9.1 Visualization of the Image Cube ................... 445
9.9.2 Training for Classification ....................... 447
9.9.3 Feature Extraction from Hyperspectral Data ........ 447
Image residuals ................................... 447
Absorption-band parameters ........................ 448
Spectral derivative ratios ........................ 448
Spectral fingerprints ............................. 449
9.9.4 Classification Algorithms for Hyperspectral
Data .............................................. 450
Binary encoding ................................... 452
Spectral-angle mapping ............................ 452
Orthogonal Subspace Projection (OSP) .............. 454
9.10 Summary .................................................. 455
9.11 Exercises ................................................ 456
APPENDIX A Sensor Acronyms .................................... 457
APPENDIX В 1-D and 2-D Functions .............................. 461
References .................................................... 467
Index ......................................................... 509
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