Overview of the Book xv
Part One Image Processing 1
1 Digital Image and Display 3
1.1 What is a digital image? 3
1.2 Digital image display 4
1.2.1 Monochromatic display 4
1.2.2 Tristimulus colour theory and RGB colour display 5
1.2.3 Pseudo colour display 7
1.3 Some key points 8
Questions 8
2 Point Operations (Contrast Enhancement) 9
2.1 Histogram modification and lookup table 9
2.2 Linear contrast enhancement 11
2.2.1 Derivation of a linear function from two points 12
2.3 Logarithmic and exponential contrast enhancement 13
2.3.1 Logarithmic contrast enhancement 13
2.3.2 Exponential contrast enhancement 14
2.4 Histogram equalization 14
2.5 Histogram matching and Gaussian stretch 15
2.6 Balance contrast enhancement technique 16
2.6.1 *Derivation of coefficients, a, b and c for a BCET parabolic function 16
2.7 Clipping in contrast enhancement 18
2.8 Tips for interactive contrast enhancement 18
Questions 19
3 Algebraic Operations (Multi-image Point Operations) 21
3.1 Image addition 21
3.2 Image subtraction (differencing) 22
3.3 Image multiplication 22
3.4 Image division (ratio) 24
3.5 Index derivation and supervised enhancement 26
3.5.1 Vegetation indices 27
3.5.2 Iron oxide ratio index 28
3.5.3 TM clay (hydrated) mineral ratio index 29
3.6 Standardization and logarithmic residual 29
3.7 Simulated reflectance 29
3.7.1 Analysis of solar radiation balance and simulated irradiance 29
3.7.2 Simulated spectral reflectance image 30
3.7.3 Calculation of weights 31
3.7.4 Example: ATM simulated reflectance colour composite 32
3.7.5 Comparison with ratio and logarithmic residual techniques 33
3.8 Summary 34
Questions 35
4 Filtering and Neighbourhood Processing 37
4.1 Fourier transform: understanding filtering in image frequency 37
4.2 Concepts of convolution for image filtering 39
4.3 Low-pass filters (smoothing) 40
4.3.1 Gaussian filter 41
4.3.2 The k nearest mean filter 42
4.3.3 Median filter 42
4.3.4 Adaptive median filter 42
4.3.5 The k nearest median filter 43
4.3.6 Mode (majority) filter 43
4.3.7 Conditional smoothing filter 43
4.4 High-pass filters (edge enhancement) 44
4.4.1 Gradient filters 45
4.4.2 Laplacian filters 46
4.4.3 Edge-sharpening filters 47
4.5 Local contrast enhancement 48
4.6 *FFT selective and adaptive filtering 48
4.6.1 FFT selective filtering 49
4.6.2 FFT adaptive filtering 51
4.7 Summary 54
Questions 54
5 RGB–IHS Transformation 57
5.1 Colour coordinate transformation 57
5.2 IHS decorrelation stretch 59
5.3 Direct decorrelation stretch technique 61
5.4 Hue RGB colour composites 63
5.5 *Derivation of RGB–IHS and IHS–RGB transformations based on 3D geometry of the RGB colour cube 65
5.5.1 Derivation of RGB–IHS Transformation 65
5.5.2 Derivation of IHS–RGB transformation 66
5.6 *Mathematical proof of DDS and its properties 67
5.6.1 Mathematical proof of DDS 67
5.6.2 The properties of DDS 68
5.7 Summary 70
Questions 70
6 Image Fusion Techniques 71
6.1 RGB–IHS transformation as a tool for data fusion 71
6.2 Brovey transform (intensity modulation) 73
6.3 Smoothing-filter-based intensity modulation 73
6.3.1 The principle of SFIM 74
6.3.2 Merits and limitation of SFIM 75
6.4 Summary 76
Questions 76
7 Principal Component Analysis 77
7.1 Principle of PCA 77
7.2 Principal component images and colour composition 80
7.3 Selective PCA for PC colour composition 82
7.3.1 Dimensionality and colour confusion reduction 82
7.3.2 Spectral contrast mapping 83
7.3.3 FPCS spectral contrast mapping 84
7.4 Decorrelation stretch 85
7.5 Physical-property-orientated coordinate transformation and tasselled cap transformation 85
7.6 Statistic methods for band selection 88
7.6.1 Review of Chavez et al.’s and Sheffield’s methods 88
7.6.2 Index of three-dimensionality 89
7.7 Remarks 89
Questions 90
8 Image Classification 91
8.1 Approaches of statistical classification 91
8.1.1 Unsupervised classification 91
8.1.2 Supervised classification 91
8.1.3 Classification processing and implementation 92
8.1.4 Summary of classification approaches 92
8.2 Unsupervised classification (iterative clustering) 92
8.2.1 Iterative clustering algorithms 92
8.2.2 Feature space iterative clustering 93
8.2.3 Seed selection 94
8.2.4 Cluster splitting along PC1 95
8.3 Supervised classification 96
8.3.1 Generic algorithm of supervised classification 96
8.3.2 Spectral angle mapping classification 96
8.4 Decision rules: dissimilarity functions 97
8.4.1 Box classifier 97
8.4.2 Euclidean distance: simplified maximum likelihood 98
8.4.3 Maximum likelihood 98
8.4.4 *Optimal multiple point reassignment 98
8.5 Post-classification processing: smoothing and accuracy assessment 99
8.5.1 Class smoothing process 99
8.5.2 Classification accuracy assessment 100
8.6 Summary 102
Questions 102
9 Image Geometric Operations 105
9.1 Image geometric deformation 105
9.1.1 Platform flight coordinates, sensor status and imaging geometry 105
9.1.2 Earth rotation and curvature 107
9.2 Polynomial deformation model and image warping co-registration 108
9.2.1 Derivation of deformation model 109
9.2.2 Pixel DN resampling 110
9.3 GCP selection and automation 111
9.3.1 Manual and semi-automatic GCP selection 111
9.3.2 *Towards automatic GCP selection 111
9.4 *Optical flow image co-registration to sub-pixel accuracy 113
9.4.1 Basics of phase correlation 113
9.4.2 Basic scheme of pixel-to-pixel image co-registration 114
9.4.3 The median shift propagation technique 115
9.4.4 Summary of the refined pixel-to-pixel image co-registration and assessment 117
9.5 Summary 118
Questions 119
10 *Introduction to Interferometric Synthetic Aperture Radar Techniques 121
10.1 The principle of a radar interferometer 121
10.2 Radar interferogram and DEM 123
10.3 Differential InSAR and deformation measurement 125
10.4 Multi-temporal coherence image and random change detection 127
10.5 Spatial decorrelation and ratio coherence technique 129
10.6 Fringe smoothing filter 132
10.7 Summary 132
Questions 134
Part Two Geographical Information Systems 135
11 Geographical Information Systems 137
11.1 Introduction 137
11.2 Software tools 138
11.3 GIS, cartography and thematic mapping 138
11.4 Standards, interoperability and metadata 139
11.5 GIS and the Internet 140
12 Data Models and Structures 141
12.1 Introducing spatial data in representing geographic features 141
12.2 How are spatial data different from other digital data? 141
12.3 Attributes and measurement scales 142
12.4 Fundamental data structures 143
12.5 Raster data 143
12.5.1 Data quantization and storage 143
12.5.2 Spatial variability 145
12.5.3 Representing spatial relationships 145
12.5.4 The effect of resolution 146
12.5.5 Representing surfaces 147
12.6 Vector data 147
12.6.1 Representing logical relationships 148
12.6.2 Extending the vector data model 153
12.6.3 Representing surfaces 155
12.7 Conversion between data models and structures 157
12.7.1 Vector to raster conversion (rasterization) 158
12.7.2 Raster to vector conversion (vectorization) 160
12.8 Summary 161
Questions 162
13 Defining a Coordinate Space 163
13.1 Introduction 163
13.2 Datums and projections 163
13.2.1 Describing and measuring the Earth 164
13.2.2 Measuring height: the geoid 165
13.2.3 Coordinate systems 166
13.2.4 Datums 166
13.2.5 Geometric distortions and projection models 167
13.2.6 Major map projections 169
13.2.7 Projection specification 172
13.3 How coordinate information is stored and accessed 173
13.4 Selecting appropriate coordinate systems 174
Questions 175
14 Operations 177
14.1 Introducing operations on spatial data 177
14.2 Map algebra concepts 178
14.2.1 Working with null data 178
14.2.2 Logical and conditional processing 179
14.2.3 Other types of operator 179
14.3 Local operations 181
14.3.1 Primary operations 181
14.3.2 Unary operations 182
14.3.3 Binary operations 184
14.3.4 N-ary operations 185
14.4 Neighbourhood operations 185
14.4.1 Local neighbourhood 185
14.4.2 Extended neighbourhood 191
14.5 Vector equivalents to raster map algebra 192
14.6 Summary 194
Questions 195
15 Extracting Information from Point Data: Geostatistics 197
15.1 Introduction 197
15.2 Understanding the data 198
15.2.1 Histograms 198
15.2.2 Spatial autocorrelation 198
15.2.3 Variograms 199
15.2.4 Underlying trends and natural barriers 200
15.3 Interpolation 201
15.3.1 Selecting sample size 201
15.3.2 Interpolation methods 202
15.3.3 Deterministic interpolators 202
15.3.4 Stochastic interpolators 207
15.4 Summary 209
Questions 209
16 Representing and Exploiting Surfaces 211
16.1 Introduction 211
16.2 Sources and uses of surface data 211
16.2.1 Digital elevation models 211
16.2.2 Vector surfaces and objects 214
16.2.3 Uses of surface data 215
16.3 Visualizing surfaces 215
16.3.1 Visualizing in two dimensions 216
16.3.2 Visualizing in three dimensions 218
16.4 Extracting surface parameters 220
16.4.1 Slope: gradient and aspect 220
16.4.2 Curvature 222
16.4.3 Surface topology: drainage networks and watersheds 225
16.4.4 Viewshed 226
16.4.5 Calculating volume 228
16.5 Summary 229
Questions 229
17 Decision Support and Uncertainty 231
17.1 Introduction 231
17.2 Decision support 231
17.3 Uncertainty 232
17.3.1 Criterion uncertainty 233
17.3.2 Threshold uncertainty 233
17.3.3 Decision rule uncertainty 234
17.4 Risk and hazard 234
17.5 Dealing with uncertainty in spatial analysis 235
17.5.1 Error assessment (criterion uncertainty) 235
17.5.2 Fuzzy membership (threshold uncertainty) 236
17.5.3 Multi-criteria decision making (decision rule uncertainty) 236
17.5.4 Error propagation and sensitivity analysis (decision rule uncertainty) 237
17.5.5 Result validation (decision rule uncertainty) 238
17.6 Summary 239
Questions 239
18 Complex Problems and Multi-Criteria Evaluation 241
18.1 Introduction 241
18.2 Different approaches and models 242
18.2.1 Knowledge-driven approach (conceptual) 242
18.2.2 Data-driven approach (empirical) 242
18.2.3 Data-driven approach (neural network) 243
18.3 Evaluation criteria 243
18.4 Deriving weighting coefficients 244
18.4.1 Rating 244
18.4.2 Ranking 245
18.4.3 Pairwise comparison 245
18.5 Multi-criteria combination methods 248
18.5.1 Boolean logical combination 248
18.5.2 Index-overlay and algebraic combination 248
18.5.3 Weights of evidence modelling based on Bayesian probability theory 249
18.5.4 Belief and Dempster–Shafer theory 251
18.5.5 Weighted factors in linear combination 252
18.5.6 Fuzzy logic 254
18.5.7 Vectorial fuzzy modelling 256
18.6 Summary 258
Questions 258
Part Three Remote Sensing Applications 259
19 Image Processing and GIS Operation Strategy 261
19.1 General image processing strategy 262
19.1.1 Preparation of basic working dataset 263
19.1.2 Image processing 266
19.1.3 Image interpretation and map composition 270
19.2 Remote-sensing-based GIS projects: from images to thematic mapping 271
19.3 An example of thematic mapping based on optimal visualization and interpretation of multi-spectral satellite imagery 272
19.3.1 Background information 272
19.3.2 Image enhancement for visual observation 274
19.3.3 Data capture and image interpretation 274
19.3.4 Map composition 278
19.4 Summary 279
Questions 280
20 Thematic Teaching Case Studies in SE Spain 281
20.1 Thematic information extraction (1): gypsum natural outcrop mapping and quarry change assessment 281
20.1.1 Data preparation and general visualization 281
20.1.2 Gypsum enhancement and extraction based on spectral analysis 283
20.1.3 Gypsum quarry changes during 1984–2000 284
20.1.4 Summary of the case study 287
20.2 Thematic information extraction (2): spectral enhancement and mineral mapping of epithermal gold alteration, and iron ore deposits in ferroan dolomite 287
20.2.1 Image datasets and data preparation 287
20.2.2 ASTER image processing and analysis for regional prospectivity 288
20.2.3 ATM image processing and analysis for target extraction 292
20.2.4 Summary 296
20.3 Remote sensing and GIS: evaluating vegetation and land-use change in the Nijar Basin, SE Spain 296
20.3.1 Introduction 296
20.3.2 Data preparation 297
20.3.3 Highlighting vegetation 298
20.3.4 Highlighting plastic greenhouses 300
20.3.5 Identifying change between different dates of observation 302
20.3.6 Summary 304
20.4 Applied remote sensing and GIS: a combined interpretive tool for regional tectonics, drainage and water resources 304
20.4.1 Introduction 304
20.4.2 Geological and hydrological setting 305
20.4.3 Case study objectives 306
20.4.4 Land use and vegetation 307
20.4.5 Lithological enhancement and discrimination 310
20.4.6 Structural enhancement and interpretation 313
20.4.7 Summary 318
Questions 320
References 321
21 Research Case Studies 323
21.1 Vegetation change in the three parallel rivers region, Yunnan province, China 323
21.1.1 Introduction 323
21.1.2 The study area and data 324
21.1.3 Methodology 324
21.1.4 Data processing 326
21.1.5 Interpretation of regional vegetation changes 328
21.1.6 Summary 332
21.2 Landslide hazard assessment in the three gorges area of the Yangtze river using ASTER imagery: Wushan–Badong–Zogui 334
21.2.1 Introduction 334
21.2.2 The study area 334
21.2.3 Methodology: multi-variable elimination and characterization 336
21.2.4 Terrestrial information extraction 339
21.2.5 DEM and topographic information extraction 344
21.2.6 Landslide hazard mapping 347
21.2.7 Summary 349
21.3 Predicting landslides using fuzzy geohazard mapping; an example from Piemonte, North-west Italy 350
21.3.1 Introduction 350
21.3.2 The study area 352
21.3.3 A holistic GIS-based approach to landslide hazard assessment 354
21.3.4 Summary 357
21.4 Land surface change detection in a desert area in Algeria using multi-temporal ERS SAR coherence images 359
21.4.1 The study area 359
21.4.2 Coherence image processing and evaluation 360
21.4.3 Image visualization and interpretation for change detection 361
21.4.4 Summary 366
Questions 366
References 366
22 Industrial Case Studies 371
22.1 Multi-criteria assessment of mineral prospectivity, in SE Greenland 371
22.1.1 Introduction and objectives 371
22.1.2 Area description 372
22.1.3 Litho-tectonic context – why the project’s concept works 373
22.1.4 Mineral deposit types evaluated 374
22.1.5 Data preparation 374
22.1.6 Multi-criteria spatial modelling 381
22.1.7 Summary 384
Acknowledgements 386
22.2 Water resource exploration in Somalia 386
22.2.1 Introduction 386
22.2.2 Data preparation 387
22.2.3 Preliminary geological enhancements and target area identification 388
22.2.4 Discrimination potential aquifer lithologies using ASTER spectral indices 390
22.2.5 Summary 397
Questions 397
References 397
Part Four Summary 399
23 Concluding Remarks 401
23.1 Image processing 401
23.2 Geographical information systems 404
23.3 Final remarks 407
Appendix A: Imaging Sensor Systems and Remote Sensing Satellites 409
A.1 Multi-spectral sensing 409
A.2 Broadband multi-spectral sensors 413
A.2.1 Digital camera 413
A.2.2 Across-track mechanical scanner 414
A.2.3 Along-track push-broom scanner 415
A.3 Thermal sensing and thermal infrared sensors 416
A.4 Hyperspectral sensors (imaging spectrometers) 417
A.5 Passive microwave sensors 418
A.6 Active sensing: SAR imaging systems 419
Appendix B: Online Resources for Information, Software and Data 425
B.1 Software – proprietary, low cost and free (shareware) 425
B.2 Information and technical information on standards, best practice, formats, techniques and various publications 426
B.3 Data sources including online satellite imagery from major suppliers, DEM data plus GIS maps and data of all kinds 426
References 429
General references 429
Image processing 429
GIS 430
Remote sensing 430
Part One References and further reading 430
Part Two References and further reading 433
Index 437
