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 Stocha.