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Image Processing and GIS for Remote Sensing: Techniques and Applications, 2nd Edition

ISBN: 978-1-118-72420-0
472 pages
March 2016, Wiley-Blackwell
Image Processing and GIS for Remote Sensing: Techniques and Applications, 2nd Edition (1118724208) cover image

Description

Following the successful publication of the 1st edition in 2009, the 2nd edition maintains its aim to provide an application-driven package of essential techniques in image processing and GIS, together with case studies for demonstration and guidance in remote sensing applications. The book therefore has a “3 in 1” structure which pinpoints the intersection between these three individual disciplines and successfully draws them together in a balanced and comprehensive manner.

The book conveys in-depth knowledge of image processing and GIS techniques in an accessible and comprehensive manner, with clear explanations and conceptual illustrations used throughout to enhance student learning. The understanding of key concepts is always emphasised with minimal assumption of prior mathematical experience.

The book is heavily based on the authors’ own research. Many of the author-designed image processing techniques are popular around the world. For instance, the SFIM technique has long been adopted by ASTRIUM for mass-production of their standard “Pan-sharpen” imagery data. The new edition also includes a completely new chapter on subpixel technology and new case studies, based on their recent research.

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Table of Contents

Overview of the book xi

Part I Image processing

1 Digital image and display 3

1.1 What is a digital image? 3

1.2 Digital image display 4

1.3 Some key points 8

1.4 Questions 8

2 Point operations (contrast enhancement) 9

2.1 Histogram modification and lookup table 9

2.2 Linear contrast enhancement (LCE) 11

2.2.1 Derivation of a linear function from two points 12

2.3 Logarithmic and exponential contrast enhancement 13

2.4 Histogram equalisation (HE) 14

2.5 Histogram matching (HM) and Gaussian stretch 15

2.6 Balance contrast enhancement technique (BCET) 16

2.7 Clipping in contrast enhancement 18

2.8 Tips for interactive contrast enhancement 18

2.9 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) 22

3.5 Index derivation and supervised enhancement 26

3.6 Standardization and logarithmic residual 29

3.7 Simulated reflectance 29

3.8 Summary 33

3.9 Questions 34

4 Filtering and neighbourhood processing 35

4.1 FT: Understanding filtering in image frequency 35

4.2 Concepts of convolution for image filtering 37

4.3 Low pass filters (smoothing) 38

4.4 High pass filters (edge enhancement) 42

4.5 Local contrast enhancement 45

4.6 FFT selective and adaptive filtering 46

4.7 Summary 52

4.8 Questions 52

5 RGB‐IHS transformation 55

5.1 Colour co‐ordinate transformation 55

5.2 IHS de‐correlation stretch 57

5.3 Direct de‐correlation stretch technique 58

5.4 Hue RGB colour composites 60

5.5 Derivation of RGB‐IHS and IHS‐RGB transformation based on 3D geometry of the RGB colour cube 63

5.6 Mathematical proof of DDS and its properties 65

5.7 Summary 67

5.8 Questions 67

6 Image fusion techniques 69

6.1 RGB‐IHS transformation as a tool for data fusion 69

6.2 Brovey transform (intensity modulation) 71

6.3 Smoothing filter‐based intensity modulation 71

6.4 Summary 75

6.5 Questions 75

7 Principal component analysis 77

7.1 Principle of the PCA 77

7.2 PC images and PC colour composition 79

7.3 Selective PCA for PC colour composition 82

7.4 De‐correlation stretch 84

7.5 Physical property orientated coordinate transformation and tasselled cap transformation 85

7.6 Statistical methods for band selection 87

7.7 Remarks 88

7.8 Questions 89

8 Image classification 91

8.1 Approaches of statistical classification 91

8.2 Unsupervised classification (iterative clustering) 92

8.3 Supervised classification 96

8.4 Decision rules: Dissimilarity functions 97

8.5 Post‐classification processing: Smoothing and accuracy assessment 98

8.6 Summary 101

8.7 Questions 101

9 Image geometric operations 103

9.1 Image geometric deformation 103

9.2 Polynomial deformation model and image warping co‐registration 106

9.3 GCP selection and automation of image co‐registration 109

9.3.1 Manual and semi‐automatic GCP

9.4 Summary 110

9.5 Questions 110

10 Introduction to interferometric synthetic aperture radar technique 113

10.1 The principle of a radar interferometer 113

10.2 Radar interferogram and DEM 115

10.3 Differential InSAR and deformation measurement 117

10.4 Multi‐temporal coherence image and random change detection 119

10.5 Spatial de‐correlation and ratio coherence technique 121

10.6 Fringe smoothing filter 123

10.7 Summary 124

10.8 Questions 125

11 Sub‐pixel technology and its applications 127

11.1 Phase correlation algorithm 127

11.2 PC scanning for pixel‐wise disparity estimation 132

11.3 Pixel‐wise image co‐registration 134

11.4 Very narrow‐baseline stereo matching and 3D data generation 139

11.5 Ground motion/deformation detection and estimation 143

11.6 Summary 146

Part II Geographical information systems

12 Geographical information systems 151

12.1 Introduction 151

12.2 Software tools 152

12.3 GIS, cartography and thematic mapping 152

12.4 Standards, inter‐operability and metadata 153

12.5 GIS and the internet 154

13 Data models and structures 155

13.1 Introducing spatial data in representing geographic features 155

13.2 How are spatial data different from other digital data? 155

13.3 Attributes and measurement scales 156

13.4 Fundamental data structures 156

13.5 Raster data 157

13.6 Vector data 161

13.7 Data conversion between models and structures 171

13.8 Summary 174

13.9 Questions 175

14 Defining a coordinate space 177

14.1 Introduction 177

14.2 Datums and projections 177

14.3 How coordinate information is stored and accessed 188

14.4 Selecting appropriate coordinate systems 189

14.5 Questions 189

15 Operations 191

15.1 Introducing operations on spatial data 191

15.2 Map algebra concepts 192

15.3 Local operations 194

15.4 Neighbourhood operations 199

15.5 Vector equivalents to raster map algebra 206

15.6 Automating GIS functions 209

15.7 Summary 209

15.8 Questions 210

16 Extracting information from point data: Geostatistics 211

16.1 Introduction 211

16.2 Understanding the data 211

16.2.1 Histograms 212

16.3 Interpolation 214

16.4 Summary 224

16.5 Questions 225

17 Representing and exploiting surfaces 227

17.1 Introduction 227

17.2 Sources and uses of surface data 227

17.3 Visualising surfaces 230

17.4 Extracting surface parameters 236

17.5 Summary 245

17.6 Questions 246

18 Decision support and uncertainty 247

18.1 Introduction 247

18.2 Decision support 247

18.3 Uncertainty 248

18.4 Risk and hazard 250

18.5 Dealing with uncertainty in GIS‐based spatial analysis 250

18.6 Summary 254

18.7 Questions 255

19 Complex problems and multi‐criterion evaluation 257

19.1 Introduction 257

19.2 Different approaches and models 258

19.3 Evaluation criteria 259

19.4 Deriving weighting coefficients 260

19.5 Multi‐criterion combination methods 263

19.6 Summary 272

19.7 Questions 272

Part III Remote sensing applications

20 Image processing and GIS operation strategy 275

20.1 General image processing strategy 276

20.2 Remote sensing‐based GIS projects: From images to thematic mapping 284

20.3 An example of thematic mapping based on optimal visualisation and interpretation of multi‐spectral satellite imagery 284

20.4 Summary 292

21 Thematic teaching case studies in SE Spain 293

21.1 Thematic information extraction (1): Gypsum natural outcrop mapping and quarry change assessment 293

21.2 Thematic information extraction (2): Spectral enhancement and mineral mapping of epithermal gold alteration and iron‐ore deposits in ferroan dolomite 299

21.3 Remote sensing and GIS: Evaluating vegetation and landuse change in the Nijar Basin, SE Spain 308

21.4 Applied remote sensing and GIS: A combined interpretive tool for regional tectonics, drainage and water resources in the Andarax basin 318

22 Research case studies 335

22.1 Vegetation change in the Three Parallel Rivers region, Yunnan Province, China 335

22.2 GIS modelling of earthquake damage zones using satellite imagery and digital elevation model (DEM) data 345

22.3 Predicting landslides using fuzzy geohazard mapping: An example from Piemonte, north‐west Italy 369

22.4 Land surface change detection in a desert area in Algeria using multi‐temporal ERS SAR coherence images 380

23 Industrial case studies 389

23.1 Multi‐criteria assessment of mineral prospectivity in SE Greenland 389

23.2 Water resource exploration in Somalia 405

Part IV Summary

24 Concluding remarks 419

24.1 Image processing 419

24.2 Geographic Information Systems 422

24.3 Final remarks 425

Appendix A Imaging sensor systems and remote sensing satellites 427

A.1 Multi‐spectral sensing 427

A.2 Broadband multi‐spectral sensors 431

A.2.1 Digital camera 431

A.2.2 Across‐track mechanical scanner 432

A.2.3 Along‐track push‐broom scanner 433

A.3 Thermal sensing and TIR sensors 434

A.4 Hyperspectral sensors (imaging spectrometers) 434

A.5 Passive microwave sensors 436

A.6 Active sensing: SAR imaging systems 437

Appendix B Online resources for information, software and data 441

B.1 Software – proprietary, low cost and free (shareware) 441

B.2 Information and technical information on standards, best practice, formats, techniques and various publications 441

B.3 Data sources including online satellite imagery from major suppliers, DEM data plus GIS maps and data of all kinds 442

References 443

Index 451

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Author Information

Jian Guo Liu  received a Ph.D. in 1991 in remote sensing and image processing from Imperial College London, UK and an M.Sc. in 1982 in remote sensing and geology from China University of Geosciences, Beijing, China. He is a Reader in remote sensing in the Department of Earth Science and Engineering, Imperial College London. His current research activities include: sub-pixel technology for image registration, DEM generation and change detection; image processing techniques for data fusion, filtering and InSAR; and GIS multi-data modelling for geohazard studies.

Philippa J Mason completed a BSc in Geology at Southampton University in 1987, an MSc in Remote Sensing at University College London in 1993 and a PhD in 1998 at Imperial College London. She is a lecturer in remote sensing & GIS at Imperial College London and a consultant in geological remote sensing and image interpretation. Her research interests include the application of geospatial sciences to geohazards, tectonic geomorphology, spectral geology and mineral exploration.

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