{"product_id":"essential-image-processing-and-gis-for-remote-sensing-9780470510315","title":"Essential Image Processing and GIS for Remote","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eEssential 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\"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)  \u003cp\u003e\"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)\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eOverview of the Book xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart One Image Processing 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Digital Image and Display 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 What is a digital image? 3\u003c\/p\u003e \u003cp\u003e1.2 Digital image display 4\u003c\/p\u003e \u003cp\u003e1.2.1 Monochromatic display 4\u003c\/p\u003e \u003cp\u003e1.2.2 Tristimulus colour theory and RGB colour display 5\u003c\/p\u003e \u003cp\u003e1.2.3 Pseudo colour display 7\u003c\/p\u003e \u003cp\u003e1.3 Some key points 8\u003c\/p\u003e \u003cp\u003eQuestions 8\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Point Operations (Contrast Enhancement) 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Histogram modification and lookup table 9\u003c\/p\u003e \u003cp\u003e2.2 Linear contrast enhancement 11\u003c\/p\u003e \u003cp\u003e2.2.1 Derivation of a linear function from two points 12\u003c\/p\u003e \u003cp\u003e2.3 Logarithmic and exponential contrast enhancement 13\u003c\/p\u003e \u003cp\u003e2.3.1 Logarithmic contrast enhancement 13\u003c\/p\u003e \u003cp\u003e2.3.2 Exponential contrast enhancement 14\u003c\/p\u003e \u003cp\u003e2.4 Histogram equalization 14\u003c\/p\u003e \u003cp\u003e2.5 Histogram matching and Gaussian stretch 15\u003c\/p\u003e \u003cp\u003e2.6 Balance contrast enhancement technique 16\u003c\/p\u003e \u003cp\u003e2.6.1 *Derivation of coefficients, a, b and c for a BCET parabolic function 16\u003c\/p\u003e \u003cp\u003e2.7 Clipping in contrast enhancement 18\u003c\/p\u003e \u003cp\u003e2.8 Tips for interactive contrast enhancement 18\u003c\/p\u003e \u003cp\u003eQuestions 19\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Algebraic Operations (Multi-image Point Operations) 21\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Image addition 21\u003c\/p\u003e \u003cp\u003e3.2 Image subtraction (differencing) 22\u003c\/p\u003e \u003cp\u003e3.3 Image multiplication 22\u003c\/p\u003e \u003cp\u003e3.4 Image division (ratio) 24\u003c\/p\u003e \u003cp\u003e3.5 Index derivation and supervised enhancement 26\u003c\/p\u003e \u003cp\u003e3.5.1 Vegetation indices 27\u003c\/p\u003e \u003cp\u003e3.5.2 Iron oxide ratio index 28\u003c\/p\u003e \u003cp\u003e3.5.3 TM clay (hydrated) mineral ratio index 29\u003c\/p\u003e \u003cp\u003e3.6 Standardization and logarithmic residual 29\u003c\/p\u003e \u003cp\u003e3.7 Simulated reflectance 29\u003c\/p\u003e \u003cp\u003e3.7.1 Analysis of solar radiation balance and simulated irradiance 29\u003c\/p\u003e \u003cp\u003e3.7.2 Simulated spectral reflectance image 30\u003c\/p\u003e \u003cp\u003e3.7.3 Calculation of weights 31\u003c\/p\u003e \u003cp\u003e3.7.4 Example: ATM simulated reflectance colour composite 32\u003c\/p\u003e \u003cp\u003e3.7.5 Comparison with ratio and logarithmic residual techniques 33\u003c\/p\u003e \u003cp\u003e3.8 Summary 34\u003c\/p\u003e \u003cp\u003eQuestions 35\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Filtering and Neighbourhood Processing 37\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Fourier transform: understanding filtering in image frequency 37\u003c\/p\u003e \u003cp\u003e4.2 Concepts of convolution for image filtering 39\u003c\/p\u003e \u003cp\u003e4.3 Low-pass filters (smoothing) 40\u003c\/p\u003e \u003cp\u003e4.3.1 Gaussian filter 41\u003c\/p\u003e \u003cp\u003e4.3.2 The \u003ci\u003ek\u003c\/i\u003e nearest mean filter 42\u003c\/p\u003e \u003cp\u003e4.3.3 Median filter 42\u003c\/p\u003e \u003cp\u003e4.3.4 Adaptive median filter 42\u003c\/p\u003e \u003cp\u003e4.3.5 The \u003ci\u003ek\u003c\/i\u003e nearest median filter 43\u003c\/p\u003e \u003cp\u003e4.3.6 Mode (majority) filter 43\u003c\/p\u003e \u003cp\u003e4.3.7 Conditional smoothing filter 43\u003c\/p\u003e \u003cp\u003e4.4 High-pass filters (edge enhancement) 44\u003c\/p\u003e \u003cp\u003e4.4.1 Gradient filters 45\u003c\/p\u003e \u003cp\u003e4.4.2 Laplacian filters 46\u003c\/p\u003e \u003cp\u003e4.4.3 Edge-sharpening filters 47\u003c\/p\u003e \u003cp\u003e4.5 Local contrast enhancement 48\u003c\/p\u003e \u003cp\u003e4.6 *FFT selective and adaptive filtering 48\u003c\/p\u003e \u003cp\u003e4.6.1 FFT selective filtering 49\u003c\/p\u003e \u003cp\u003e4.6.2 FFT adaptive filtering 51\u003c\/p\u003e \u003cp\u003e4.7 Summary 54\u003c\/p\u003e \u003cp\u003eQuestions 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 RGB–IHS Transformation 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Colour coordinate transformation 57\u003c\/p\u003e \u003cp\u003e5.2 IHS decorrelation stretch 59\u003c\/p\u003e \u003cp\u003e5.3 Direct decorrelation stretch technique 61\u003c\/p\u003e \u003cp\u003e5.4 Hue RGB colour composites 63\u003c\/p\u003e \u003cp\u003e5.5 *Derivation of RGB–IHS and IHS–RGB transformations based on 3D geometry of the RGB colour cube 65\u003c\/p\u003e \u003cp\u003e5.5.1 Derivation of RGB–IHS Transformation 65\u003c\/p\u003e \u003cp\u003e5.5.2 Derivation of IHS–RGB transformation 66\u003c\/p\u003e \u003cp\u003e5.6 *Mathematical proof of DDS and its properties 67\u003c\/p\u003e \u003cp\u003e5.6.1 Mathematical proof of DDS 67\u003c\/p\u003e \u003cp\u003e5.6.2 The properties of DDS 68\u003c\/p\u003e \u003cp\u003e5.7 Summary 70\u003c\/p\u003e \u003cp\u003eQuestions 70\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Image Fusion Techniques 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 RGB–IHS transformation as a tool for data fusion 71\u003c\/p\u003e \u003cp\u003e6.2 Brovey transform (intensity modulation) 73\u003c\/p\u003e \u003cp\u003e6.3 Smoothing-filter-based intensity modulation 73\u003c\/p\u003e \u003cp\u003e6.3.1 The principle of SFIM 74\u003c\/p\u003e \u003cp\u003e6.3.2 Merits and limitation of SFIM 75\u003c\/p\u003e \u003cp\u003e6.4 Summary 76\u003c\/p\u003e \u003cp\u003eQuestions 76\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Principal Component Analysis 77\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Principle of PCA 77\u003c\/p\u003e \u003cp\u003e7.2 Principal component images and colour composition 80\u003c\/p\u003e \u003cp\u003e7.3 Selective PCA for PC colour composition 82\u003c\/p\u003e \u003cp\u003e7.3.1 Dimensionality and colour confusion reduction 82\u003c\/p\u003e \u003cp\u003e7.3.2 Spectral contrast mapping 83\u003c\/p\u003e \u003cp\u003e7.3.3 FPCS spectral contrast mapping 84\u003c\/p\u003e \u003cp\u003e7.4 Decorrelation stretch 85\u003c\/p\u003e \u003cp\u003e7.5 Physical-property-orientated coordinate transformation and tasselled cap transformation 85\u003c\/p\u003e \u003cp\u003e7.6 Statistic methods for band selection 88\u003c\/p\u003e \u003cp\u003e7.6.1 Review of Chavez et al.’s and Sheffield’s methods 88\u003c\/p\u003e \u003cp\u003e7.6.2 Index of three-dimensionality 89\u003c\/p\u003e \u003cp\u003e7.7 Remarks 89\u003c\/p\u003e \u003cp\u003eQuestions 90\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Image Classification 91\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Approaches of statistical classification 91\u003c\/p\u003e \u003cp\u003e8.1.1 Unsupervised classification 91\u003c\/p\u003e \u003cp\u003e8.1.2 Supervised classification 91\u003c\/p\u003e \u003cp\u003e8.1.3 Classification processing and implementation 92\u003c\/p\u003e \u003cp\u003e8.1.4 Summary of classification approaches 92\u003c\/p\u003e \u003cp\u003e8.2 Unsupervised classification (iterative clustering) 92\u003c\/p\u003e \u003cp\u003e8.2.1 Iterative clustering algorithms 92\u003c\/p\u003e \u003cp\u003e8.2.2 Feature space iterative clustering 93\u003c\/p\u003e \u003cp\u003e8.2.3 Seed selection 94\u003c\/p\u003e \u003cp\u003e8.2.4 Cluster splitting along PC1 95\u003c\/p\u003e \u003cp\u003e8.3 Supervised classification 96\u003c\/p\u003e \u003cp\u003e8.3.1 Generic algorithm of supervised classification 96\u003c\/p\u003e \u003cp\u003e8.3.2 Spectral angle mapping classification 96\u003c\/p\u003e \u003cp\u003e8.4 Decision rules: dissimilarity functions 97\u003c\/p\u003e \u003cp\u003e8.4.1 Box classifier 97\u003c\/p\u003e \u003cp\u003e8.4.2 Euclidean distance: simplified maximum likelihood 98\u003c\/p\u003e \u003cp\u003e8.4.3 Maximum likelihood 98\u003c\/p\u003e \u003cp\u003e8.4.4 *Optimal multiple point reassignment 98\u003c\/p\u003e \u003cp\u003e8.5 Post-classification processing: smoothing and accuracy assessment 99\u003c\/p\u003e \u003cp\u003e8.5.1 Class smoothing process 99\u003c\/p\u003e \u003cp\u003e8.5.2 Classification accuracy assessment 100\u003c\/p\u003e \u003cp\u003e8.6 Summary 102\u003c\/p\u003e \u003cp\u003eQuestions 102\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Image Geometric Operations 105\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Image geometric deformation 105\u003c\/p\u003e \u003cp\u003e9.1.1 Platform flight coordinates, sensor status and imaging geometry 105\u003c\/p\u003e \u003cp\u003e9.1.2 Earth rotation and curvature 107\u003c\/p\u003e \u003cp\u003e9.2 Polynomial deformation model and image warping co-registration 108\u003c\/p\u003e \u003cp\u003e9.2.1 Derivation of deformation model 109\u003c\/p\u003e \u003cp\u003e9.2.2 Pixel DN resampling 110\u003c\/p\u003e \u003cp\u003e9.3 GCP selection and automation 111\u003c\/p\u003e \u003cp\u003e9.3.1 Manual and semi-automatic GCP selection 111\u003c\/p\u003e \u003cp\u003e9.3.2 *Towards automatic GCP selection 111\u003c\/p\u003e \u003cp\u003e9.4 *Optical flow image co-registration to sub-pixel accuracy 113\u003c\/p\u003e \u003cp\u003e9.4.1 Basics of phase correlation 113\u003c\/p\u003e \u003cp\u003e9.4.2 Basic scheme of pixel-to-pixel image co-registration 114\u003c\/p\u003e \u003cp\u003e9.4.3 The median shift propagation technique 115\u003c\/p\u003e \u003cp\u003e9.4.4 Summary of the refined pixel-to-pixel image co-registration and assessment 117\u003c\/p\u003e \u003cp\u003e9.5 Summary 118\u003c\/p\u003e \u003cp\u003eQuestions 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 *Introduction to Interferometric Synthetic Aperture Radar Techniques 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 The principle of a radar interferometer 121\u003c\/p\u003e \u003cp\u003e10.2 Radar interferogram and DEM 123\u003c\/p\u003e \u003cp\u003e10.3 Differential InSAR and deformation measurement 125\u003c\/p\u003e \u003cp\u003e10.4 Multi-temporal coherence image and random change detection 127\u003c\/p\u003e \u003cp\u003e10.5 Spatial decorrelation and ratio coherence technique 129\u003c\/p\u003e \u003cp\u003e10.6 Fringe smoothing filter 132\u003c\/p\u003e \u003cp\u003e10.7 Summary 132\u003c\/p\u003e \u003cp\u003eQuestions 134\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Two Geographical Information Systems 135\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Geographical Information Systems 137\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 137\u003c\/p\u003e \u003cp\u003e11.2 Software tools 138\u003c\/p\u003e \u003cp\u003e11.3 GIS, cartography and thematic mapping 138\u003c\/p\u003e \u003cp\u003e11.4 Standards, interoperability and metadata 139\u003c\/p\u003e \u003cp\u003e11.5 GIS and the Internet 140\u003c\/p\u003e \u003cp\u003e12 Data Models and Structures 141\u003c\/p\u003e \u003cp\u003e12.1 Introducing spatial data in representing geographic features 141\u003c\/p\u003e \u003cp\u003e12.2 How are spatial data different from other digital data? 141\u003c\/p\u003e \u003cp\u003e12.3 Attributes and measurement scales 142\u003c\/p\u003e \u003cp\u003e12.4 Fundamental data structures 143\u003c\/p\u003e \u003cp\u003e12.5 Raster data 143\u003c\/p\u003e \u003cp\u003e12.5.1 Data quantization and storage 143\u003c\/p\u003e \u003cp\u003e12.5.2 Spatial variability 145\u003c\/p\u003e \u003cp\u003e12.5.3 Representing spatial relationships 145\u003c\/p\u003e \u003cp\u003e12.5.4 The effect of resolution 146\u003c\/p\u003e \u003cp\u003e12.5.5 Representing surfaces 147\u003c\/p\u003e \u003cp\u003e12.6 Vector data 147\u003c\/p\u003e \u003cp\u003e12.6.1 Representing logical relationships 148\u003c\/p\u003e \u003cp\u003e12.6.2 Extending the vector data model 153\u003c\/p\u003e \u003cp\u003e12.6.3 Representing surfaces 155\u003c\/p\u003e \u003cp\u003e12.7 Conversion between data models and structures 157\u003c\/p\u003e \u003cp\u003e12.7.1 Vector to raster conversion (rasterization) 158\u003c\/p\u003e \u003cp\u003e12.7.2 Raster to vector conversion (vectorization) 160\u003c\/p\u003e \u003cp\u003e12.8 Summary 161\u003c\/p\u003e \u003cp\u003eQuestions 162\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Defining a Coordinate Space 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 163\u003c\/p\u003e \u003cp\u003e13.2 Datums and projections 163\u003c\/p\u003e \u003cp\u003e13.2.1 Describing and measuring the Earth 164\u003c\/p\u003e \u003cp\u003e13.2.2 Measuring height: the geoid 165\u003c\/p\u003e \u003cp\u003e13.2.3 Coordinate systems 166\u003c\/p\u003e \u003cp\u003e13.2.4 Datums 166\u003c\/p\u003e \u003cp\u003e13.2.5 Geometric distortions and projection models 167\u003c\/p\u003e \u003cp\u003e13.2.6 Major map projections 169\u003c\/p\u003e \u003cp\u003e13.2.7 Projection specification 172\u003c\/p\u003e \u003cp\u003e13.3 How coordinate information is stored and accessed 173\u003c\/p\u003e \u003cp\u003e13.4 Selecting appropriate coordinate systems 174\u003c\/p\u003e \u003cp\u003eQuestions 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Operations 177\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introducing operations on spatial data 177\u003c\/p\u003e \u003cp\u003e14.2 Map algebra concepts 178\u003c\/p\u003e \u003cp\u003e14.2.1 Working with null data 178\u003c\/p\u003e \u003cp\u003e14.2.2 Logical and conditional processing 179\u003c\/p\u003e \u003cp\u003e14.2.3 Other types of operator 179\u003c\/p\u003e \u003cp\u003e14.3 Local operations 181\u003c\/p\u003e \u003cp\u003e14.3.1 Primary operations 181\u003c\/p\u003e \u003cp\u003e14.3.2 Unary operations 182\u003c\/p\u003e \u003cp\u003e14.3.3 Binary operations 184\u003c\/p\u003e \u003cp\u003e14.3.4 N-ary operations 185\u003c\/p\u003e \u003cp\u003e14.4 Neighbourhood operations 185\u003c\/p\u003e \u003cp\u003e14.4.1 Local neighbourhood 185\u003c\/p\u003e \u003cp\u003e14.4.2 Extended neighbourhood 191\u003c\/p\u003e \u003cp\u003e14.5 Vector equivalents to raster map algebra 192\u003c\/p\u003e \u003cp\u003e14.6 Summary 194\u003c\/p\u003e \u003cp\u003eQuestions 195\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Extracting Information from Point Data: Geostatistics 197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 197\u003c\/p\u003e \u003cp\u003e15.2 Understanding the data 198\u003c\/p\u003e \u003cp\u003e15.2.1 Histograms 198\u003c\/p\u003e \u003cp\u003e15.2.2 Spatial autocorrelation 198\u003c\/p\u003e \u003cp\u003e15.2.3 Variograms 199\u003c\/p\u003e \u003cp\u003e15.2.4 Underlying trends and natural barriers 200\u003c\/p\u003e \u003cp\u003e15.3 Interpolation 201\u003c\/p\u003e \u003cp\u003e15.3.1 Selecting sample size 201\u003c\/p\u003e \u003cp\u003e15.3.2 Interpolation methods 202\u003c\/p\u003e \u003cp\u003e15.3.3 Deterministic interpolators 202\u003c\/p\u003e \u003cp\u003e15.3.4 Stochastic interpolators 207\u003c\/p\u003e \u003cp\u003e15.4 Summary 209\u003c\/p\u003e \u003cp\u003eQuestions 209\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Representing and Exploiting Surfaces 211\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 211\u003c\/p\u003e \u003cp\u003e16.2 Sources and uses of surface data 211\u003c\/p\u003e \u003cp\u003e16.2.1 Digital elevation models 211\u003c\/p\u003e \u003cp\u003e16.2.2 Vector surfaces and objects 214\u003c\/p\u003e \u003cp\u003e16.2.3 Uses of surface data 215\u003c\/p\u003e \u003cp\u003e16.3 Visualizing surfaces 215\u003c\/p\u003e \u003cp\u003e16.3.1 Visualizing in two dimensions 216\u003c\/p\u003e \u003cp\u003e16.3.2 Visualizing in three dimensions 218\u003c\/p\u003e \u003cp\u003e16.4 Extracting surface parameters 220\u003c\/p\u003e \u003cp\u003e16.4.1 Slope: gradient and aspect 220\u003c\/p\u003e \u003cp\u003e16.4.2 Curvature 222\u003c\/p\u003e \u003cp\u003e16.4.3 Surface topology: drainage networks and watersheds 225\u003c\/p\u003e \u003cp\u003e16.4.4 Viewshed 226\u003c\/p\u003e \u003cp\u003e16.4.5 Calculating volume 228\u003c\/p\u003e \u003cp\u003e16.5 Summary 229\u003c\/p\u003e \u003cp\u003eQuestions 229\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Decision Support and Uncertainty 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 231\u003c\/p\u003e \u003cp\u003e17.2 Decision support 231\u003c\/p\u003e \u003cp\u003e17.3 Uncertainty 232\u003c\/p\u003e \u003cp\u003e17.3.1 Criterion uncertainty 233\u003c\/p\u003e \u003cp\u003e17.3.2 Threshold uncertainty 233\u003c\/p\u003e \u003cp\u003e17.3.3 Decision rule uncertainty 234\u003c\/p\u003e \u003cp\u003e17.4 Risk and hazard 234\u003c\/p\u003e \u003cp\u003e17.5 Dealing with uncertainty in spatial analysis 235\u003c\/p\u003e \u003cp\u003e17.5.1 Error assessment (criterion uncertainty) 235\u003c\/p\u003e \u003cp\u003e17.5.2 Fuzzy membership (threshold uncertainty) 236\u003c\/p\u003e \u003cp\u003e17.5.3 Multi-criteria decision making (decision rule uncertainty) 236\u003c\/p\u003e \u003cp\u003e17.5.4 Error propagation and sensitivity analysis (decision rule uncertainty) 237\u003c\/p\u003e \u003cp\u003e17.5.5 Result validation (decision rule uncertainty) 238\u003c\/p\u003e \u003cp\u003e17.6 Summary 239\u003c\/p\u003e \u003cp\u003eQuestions 239\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Complex Problems and Multi-Criteria Evaluation 241\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 241\u003c\/p\u003e \u003cp\u003e18.2 Different approaches and models 242\u003c\/p\u003e \u003cp\u003e18.2.1 Knowledge-driven approach (conceptual) 242\u003c\/p\u003e \u003cp\u003e18.2.2 Data-driven approach (empirical) 242\u003c\/p\u003e \u003cp\u003e18.2.3 Data-driven approach (neural network) 243\u003c\/p\u003e \u003cp\u003e18.3 Evaluation criteria 243\u003c\/p\u003e \u003cp\u003e18.4 Deriving weighting coefficients 244\u003c\/p\u003e \u003cp\u003e18.4.1 Rating 244\u003c\/p\u003e \u003cp\u003e18.4.2 Ranking 245\u003c\/p\u003e \u003cp\u003e18.4.3 Pairwise comparison 245\u003c\/p\u003e \u003cp\u003e18.5 Multi-criteria combination methods 248\u003c\/p\u003e \u003cp\u003e18.5.1 Boolean logical combination 248\u003c\/p\u003e \u003cp\u003e18.5.2 Index-overlay and algebraic combination 248\u003c\/p\u003e \u003cp\u003e18.5.3 Weights of evidence modelling based on Bayesian probability theory 249\u003c\/p\u003e \u003cp\u003e18.5.4 Belief and Dempster–Shafer theory 251\u003c\/p\u003e \u003cp\u003e18.5.5 Weighted factors in linear combination 252\u003c\/p\u003e \u003cp\u003e18.5.6 Fuzzy logic 254\u003c\/p\u003e \u003cp\u003e18.5.7 Vectorial fuzzy modelling 256\u003c\/p\u003e \u003cp\u003e18.6 Summary 258\u003c\/p\u003e \u003cp\u003eQuestions 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Three Remote Sensing Applications 259\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Image Processing and GIS Operation Strategy 261\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e19.1 General image processing strategy 262\u003c\/p\u003e \u003cp\u003e19.1.1 Preparation of basic working dataset 263\u003c\/p\u003e \u003cp\u003e19.1.2 Image processing 266\u003c\/p\u003e \u003cp\u003e19.1.3 Image interpretation and map composition 270\u003c\/p\u003e \u003cp\u003e19.2 Remote-sensing-based GIS projects: from images to thematic mapping 271\u003c\/p\u003e \u003cp\u003e19.3 An example of thematic mapping based on optimal visualization and interpretation of multi-spectral satellite imagery 272\u003c\/p\u003e \u003cp\u003e19.3.1 Background information 272\u003c\/p\u003e \u003cp\u003e19.3.2 Image enhancement for visual observation 274\u003c\/p\u003e \u003cp\u003e19.3.3 Data capture and image interpretation 274\u003c\/p\u003e \u003cp\u003e19.3.4 Map composition 278\u003c\/p\u003e \u003cp\u003e19.4 Summary 279\u003c\/p\u003e \u003cp\u003eQuestions 280\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Thematic Teaching Case Studies in SE Spain 281\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e20.1 Thematic information extraction (1): gypsum natural outcrop mapping and quarry change assessment 281\u003c\/p\u003e \u003cp\u003e20.1.1 Data preparation and general visualization 281\u003c\/p\u003e \u003cp\u003e20.1.2 Gypsum enhancement and extraction based on spectral analysis 283\u003c\/p\u003e \u003cp\u003e20.1.3 Gypsum quarry changes during 1984–2000 284\u003c\/p\u003e \u003cp\u003e20.1.4 Summary of the case study 287\u003c\/p\u003e \u003cp\u003e20.2 Thematic information extraction (2): spectral enhancement and mineral mapping of epithermal gold alteration, and iron ore deposits in ferroan dolomite 287\u003c\/p\u003e \u003cp\u003e20.2.1 Image datasets and data preparation 287\u003c\/p\u003e \u003cp\u003e20.2.2 ASTER image processing and analysis for regional prospectivity 288\u003c\/p\u003e \u003cp\u003e20.2.3 ATM image processing and analysis for target extraction 292\u003c\/p\u003e \u003cp\u003e20.2.4 Summary 296\u003c\/p\u003e \u003cp\u003e20.3 Remote sensing and GIS: evaluating vegetation and land-use change in the Nijar Basin, SE Spain 296\u003c\/p\u003e \u003cp\u003e20.3.1 Introduction 296\u003c\/p\u003e \u003cp\u003e20.3.2 Data preparation 297\u003c\/p\u003e \u003cp\u003e20.3.3 Highlighting vegetation 298\u003c\/p\u003e \u003cp\u003e20.3.4 Highlighting plastic greenhouses 300\u003c\/p\u003e \u003cp\u003e20.3.5 Identifying change between different dates of observation 302\u003c\/p\u003e \u003cp\u003e20.3.6 Summary 304\u003c\/p\u003e \u003cp\u003e20.4 Applied remote sensing and GIS: a combined interpretive tool for regional tectonics, drainage and water resources 304\u003c\/p\u003e \u003cp\u003e20.4.1 Introduction 304\u003c\/p\u003e \u003cp\u003e20.4.2 Geological and hydrological setting 305\u003c\/p\u003e \u003cp\u003e20.4.3 Case study objectives 306\u003c\/p\u003e \u003cp\u003e20.4.4 Land use and vegetation 307\u003c\/p\u003e \u003cp\u003e20.4.5 Lithological enhancement and discrimination 310\u003c\/p\u003e \u003cp\u003e20.4.6 Structural enhancement and interpretation 313\u003c\/p\u003e \u003cp\u003e20.4.7 Summary 318\u003c\/p\u003e \u003cp\u003eQuestions 320\u003c\/p\u003e \u003cp\u003eReferences 321\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21 Research Case Studies 323\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e21.1 Vegetation change in the three parallel rivers region, Yunnan province, China 323\u003c\/p\u003e \u003cp\u003e21.1.1 Introduction 323\u003c\/p\u003e \u003cp\u003e21.1.2 The study area and data 324\u003c\/p\u003e \u003cp\u003e21.1.3 Methodology 324\u003c\/p\u003e \u003cp\u003e21.1.4 Data processing 326\u003c\/p\u003e \u003cp\u003e21.1.5 Interpretation of regional vegetation changes 328\u003c\/p\u003e \u003cp\u003e21.1.6 Summary 332\u003c\/p\u003e \u003cp\u003e21.2 Landslide hazard assessment in the three gorges area of the Yangtze river using ASTER imagery: Wushan–Badong–Zogui 334\u003c\/p\u003e \u003cp\u003e21.2.1 Introduction 334\u003c\/p\u003e \u003cp\u003e21.2.2 The study area 334\u003c\/p\u003e \u003cp\u003e21.2.3 Methodology: multi-variable elimination and characterization 336\u003c\/p\u003e \u003cp\u003e21.2.4 Terrestrial information extraction 339\u003c\/p\u003e \u003cp\u003e21.2.5 DEM and topographic information extraction 344\u003c\/p\u003e \u003cp\u003e21.2.6 Landslide hazard mapping 347\u003c\/p\u003e \u003cp\u003e21.2.7 Summary 349\u003c\/p\u003e \u003cp\u003e21.3 Predicting landslides using fuzzy geohazard mapping; an example from Piemonte, North-west Italy 350\u003c\/p\u003e \u003cp\u003e21.3.1 Introduction 350\u003c\/p\u003e \u003cp\u003e21.3.2 The study area 352\u003c\/p\u003e \u003cp\u003e21.3.3 A holistic GIS-based approach to landslide hazard assessment 354\u003c\/p\u003e \u003cp\u003e21.3.4 Summary 357\u003c\/p\u003e \u003cp\u003e21.4 Land surface change detection in a desert area in Algeria using multi-temporal ERS SAR coherence images 359\u003c\/p\u003e \u003cp\u003e21.4.1 The study area 359\u003c\/p\u003e \u003cp\u003e21.4.2 Coherence image processing and evaluation 360\u003c\/p\u003e \u003cp\u003e21.4.3 Image visualization and interpretation for change detection 361\u003c\/p\u003e \u003cp\u003e21.4.4 Summary 366\u003c\/p\u003e \u003cp\u003eQuestions 366\u003c\/p\u003e \u003cp\u003eReferences 366\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22 Industrial Case Studies 371\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e22.1 Multi-criteria assessment of mineral prospectivity, in SE Greenland 371\u003c\/p\u003e \u003cp\u003e22.1.1 Introduction and objectives 371\u003c\/p\u003e \u003cp\u003e22.1.2 Area description 372\u003c\/p\u003e \u003cp\u003e22.1.3 Litho-tectonic context – why the project’s concept works 373\u003c\/p\u003e \u003cp\u003e22.1.4 Mineral deposit types evaluated 374\u003c\/p\u003e \u003cp\u003e22.1.5 Data preparation 374\u003c\/p\u003e \u003cp\u003e22.1.6 Multi-criteria spatial modelling 381\u003c\/p\u003e \u003cp\u003e22.1.7 Summary 384\u003c\/p\u003e \u003cp\u003eAcknowledgements 386\u003c\/p\u003e \u003cp\u003e22.2 Water resource exploration in Somalia 386\u003c\/p\u003e \u003cp\u003e22.2.1 Introduction 386\u003c\/p\u003e \u003cp\u003e22.2.2 Data preparation 387\u003c\/p\u003e \u003cp\u003e22.2.3 Preliminary geological enhancements and target area identification 388\u003c\/p\u003e \u003cp\u003e22.2.4 Discrimination potential aquifer lithologies using ASTER spectral indices 390\u003c\/p\u003e \u003cp\u003e22.2.5 Summary 397\u003c\/p\u003e \u003cp\u003eQuestions 397\u003c\/p\u003e \u003cp\u003eReferences 397\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Four Summary 399\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23 Concluding Remarks 401\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e23.1 Image processing 401\u003c\/p\u003e \u003cp\u003e23.2 Geographical information systems 404\u003c\/p\u003e \u003cp\u003e23.3 Final remarks 407\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix A: Imaging Sensor Systems and Remote Sensing Satellites 409\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA.1 Multi-spectral sensing 409\u003c\/p\u003e \u003cp\u003eA.2 Broadband multi-spectral sensors 413\u003c\/p\u003e \u003cp\u003eA.2.1 Digital camera 413\u003c\/p\u003e \u003cp\u003eA.2.2 Across-track mechanical scanner 414\u003c\/p\u003e \u003cp\u003eA.2.3 Along-track push-broom scanner 415\u003c\/p\u003e \u003cp\u003eA.3 Thermal sensing and thermal infrared sensors 416\u003c\/p\u003e \u003cp\u003eA.4 Hyperspectral sensors (imaging spectrometers) 417\u003c\/p\u003e \u003cp\u003eA.5 Passive microwave sensors 418\u003c\/p\u003e \u003cp\u003eA.6 Active sensing: SAR imaging systems 419\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix B: Online Resources for Information, Software and Data 425\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eB.1 Software – proprietary, low cost and free (shareware) 425\u003c\/p\u003e \u003cp\u003eB.2 Information and technical information on standards, best practice, formats, techniques and various publications 426\u003c\/p\u003e \u003cp\u003eB.3 Data sources including online satellite imagery from major suppliers, DEM data plus GIS maps and data of all kinds 426\u003c\/p\u003e \u003cp\u003e\u003cb\u003eReferences 429\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGeneral references 429\u003c\/p\u003e \u003cp\u003eImage processing 429\u003c\/p\u003e \u003cp\u003eGIS 430\u003c\/p\u003e \u003cp\u003eRemote sensing 430\u003c\/p\u003e \u003cp\u003ePart One References and further reading 430\u003c\/p\u003e \u003cp\u003ePart Two References and further reading 433\u003c\/p\u003e \u003cp\u003eIndex 437\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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