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Essential Image Processing and GIS for Remote Sensing. Edition No. 1

  • Book
  • 460 Pages
  • July 2009
  • John Wiley and Sons Ltd
  • ID: 2325733
Essential Image Processing and GIS for Remote Sensing is an accessible overview of the subject and successfully draws together these three key areas in a balanced and comprehensive manner. The book provides an overview of essential techniques and a selection of key case studies in a variety of application areas.

Key concepts and ideas are introduced in a clear and logical manner and described through the provision of numerous relevant conceptual illustrations. Mathematical detail is kept to a minimum and only referred to where necessary for ease of understanding. Such concepts are explained through common sense terms rather than in rigorous mathematical detail when explaining image processing and GIS techniques, to enable students to grasp the essentials of a notoriously challenging subject area. 

The book is clearly divided into three parts, with the first part introducing essential image processing techniques for remote sensing. The second part looks at GIS and begins with an overview of the concepts, structures and mechanisms by which GIS operates. Finally the third part introduces Remote Sensing Applications. Throughout the book the relationships between GIS, Image Processing and Remote Sensing are clearly identified to ensure that students are able to apply the various techniques that have been covered appropriately. The latter chapters use numerous relevant case studies to illustrate various remote sensing, image processing and GIS applications in practice. 

 

Table of Contents

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

Authors

Jian Guo Liu Department of Earth Science and Eng. Philippa J. Mason Department of Earth Sciences.