Details

Essential Image Processing and GIS for Remote Sensing

2. Aufl.

von: Jian Guo Liu, Philippa J. Mason
61,99 €
Verlag:	Wiley
Format:	PDF
Veröffentl.:	10.06.2009
ISBN/EAN:	9780470746042
Sprache:	englisch
Anzahl Seiten:	464

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Beschreibungen

Titelbeschreibung

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.

Inhaltsverzeichnis

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

Rezension

"This book will allow interpreters to approach their work with a wider and deeper understanding of what has happened to imagery before it lands on their desk or computer." (Society of Exploration Geophysicists, 1 August 2011) "The authors have described the key concepts and ideas with clarity and in a logical manner and have also included numerous relevant conceptual illustrations. The book contains twenty three chapters, all of which are well written." (IAPR Newsletter, 1 July 2011)

Autorenportrait

Dr Jian Guo Liu, Senior Lecturer, Department of Earth Sciences and Engineering, Imperial College London, UK Dr Philippa? Mason, Teaching Associate, Department of Earth Sciences and Engineering, Imperial College London, UK

Back cover copy

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. This book pinpoints the overlap between the individual subjects, providing a summary of essential techniques and a selection of key case studies in a variety of application areas. The book conveys in-depth knowledge of image processing and GIS in an accessible manner, with clear explanations and conceptual illustrations used throughout to enhance student understanding. The understanding of key concepts is emphasised throughout with minimal assumption of prior mathematical experience. <ul> <li>Comprehensive coverage of new image processing techniques such as BCET, DDS and SFIM</li> <li>Includes case studies based on experience in industry projects using high level image processing and GIS</li> <li>Emphasis on easy to understand terminology with clear explanations and conceptual illustrations throughout to enhance student understanding</li> <li>Focus on earth science applications</li> <li>Extended coverage of essential mathematics for use by course tutors and lecturers highlighted throughout</li> <li>Supplementary material is available on the book’s companion website: www.wileyeurope.com/college/liu</li> </ul>