{"product_id":"essential-image-processing-and-gis-for-remote-sensing-isbn-9780470510315","title":"Essential Image Processing and GIS for Remote Sensing","description":"\u003ci\u003eEssential Image Processing and GIS for Remote Sensing\u003c\/i\u003e 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.  \u003cp\u003eKey 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. \u003c\/p\u003e \u003cp\u003eThe 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. \u003c\/p\u003e \u003cp\u003e \u003c\/p\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  \"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  \u003cp\u003e\u003cstrong\u003eDr Jian Guo Liu\u003c\/strong\u003e, Senior Lecturer, Department of Earth Sciences and Engineering, Imperial College London, UK \u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eDr Philippa? Mason\u003c\/strong\u003e, Teaching Associate, Department of Earth Sciences and Engineering, Imperial College London, UK\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989156249829,"sku":"NP9780470510315","price":93.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470510315.jpg?v=1761783024","url":"https:\/\/k12savings.com\/products\/essential-image-processing-and-gis-for-remote-sensing-isbn-9780470510315","provider":"K12savings","version":"1.0","type":"link"}