Chapter
2.6.1 Derivation of coefficients a, b and c for a BCET parabolic function (Liu 1991)
2.7 Clipping in contrast enhancement
2.8 Tips for interactive contrast enhancement
Chapter 3 Algebraic operations (multi-image point operations)
3.2 Image subtraction (differencing)
3.4 Image division (ratio)
3.5 Index derivation and supervised enhancement
3.5.2 Iron oxide ratio index
3.5.3 TM clay (hydrated) mineral ratio index
3.6 Standardization and logarithmic residual
3.7 Simulated reflectance
3.7.1 Analysis of solar radiation balance and simulated irradiance
3.7.2 Simulated spectral reflectance image
3.7.3 Calculation of weights
3.7.4 Example: ATM simulated reflectance colour composite
3.7.5 Comparison with ratio and logarithmic residual techniques
Chapter 4 Filtering and neighbourhood processing
4.1 FT: Understanding filtering in image frequency
4.2 Concepts of convolution for image filtering
4.3 Low pass filters (smoothing)
4.3.2 K nearest mean filter
4.3.4 Adaptive median filter
4.3.5 K nearest median filter
4.3.6 Mode (majority) filter
4.3.7 Conditional smoothing filters
4.4 High pass filters (edge enhancement)
4.4.3 Edge-sharpening filters
4.5 Local contrast enhancement
4.6 FFT selective and adaptive filtering
4.6.1 FFT selective filtering
4.6.2 FFT adaptive filtering
Chapter 5 RGB-IHS transformation
5.1 Colour co-ordinate transformation
5.2 IHS de-correlation stretch
5.3 Direct de-correlation stretch technique
5.4 Hue RGB colour composites
5.5 Derivation of RGB-IHS and IHS-RGB transformation based on 3D geometry of the RGB colour cube
5.5.1 Derivation of RGB-IHS transformation
5.5.2 Derivation of IHS-RGB transformation
5.6 Mathematical proof of DDS and its properties
5.6.1 Mathematical proof of DDS
5.6.2 The properties of DDS
Chapter 6 Image fusion techniques
6.1 RGB-IHS transformation as a tool for data fusion
6.2 Brovey transform (intensity modulation)
6.3 Smoothing filter-based intensity modulation
6.3.1 The principle of SFIM
6.3.2 Merits and limitations of SFIM
6.3.3 An example of SFIM pan-sharpen of Landsat 8 OLI image
Chapter 7 Principal component analysis
7.2 PC images and PC colour composition
7.3 Selective PCA for PC colour composition
7.3.1 Dimensionality and colour confusion reduction
7.3.2 Spectral contrast mapping
7.3.3 FPCS spectral contrast mapping
7.4 De-correlation stretch
7.5 Physical property orientated coordinate transformation and tasselled cap transformation
7.6 Statistical methods for band selection
7.6.1 Review of Chavez’s and Sheffield’s methods
7.6.2 Index of three-dimensionality
Chapter 8 Image classification
8.1 Approaches of statistical classification
8.1.1 Unsupervised classification
8.1.2 Supervised classification
8.1.3 Classification processing and implementation
8.1.4 Summary of classification approaches
8.2 Unsupervised classification (iterative clustering)
8.2.1 Iterative clustering algorithms
8.2.2 Feature space iterative clustering
8.2.4 Cluster splitting along PC1
8.3 Supervised classification
8.3.1 Generic algorithm of supervised classification
8.3.2 Spectral angle mapping classification
8.4 Decision rules: Dissimilarity functions
8.4.2 Euclidean distance: Simplified maximum likelihood
8.4.4 Optimal multiple point re-assignment (OMPR)
8.5 Post-classification processing: Smoothing and accuracy assessment
8.5.1 Class smoothing process
8.5.2 Classification accuracy assessment
Chapter 9 Image geometric operations
9.1 Image geometric deformation
9.1.1 Platform flight coordinates, sensor status and imaging position
9.1.2 Earth rotation and curvature
9.2 Polynomial deformation model and image warping co-registration
9.2.1 Derivation of deformation model
9.2.2 Pixel DN re-sampling
9.3 GCP selection and automation of image co-registration
9.3.1 Manual and semi-automatic GCP selection
9.3.2 Automatic image co-registration
Chapter 10 Introduction to interferometric synthetic aperture radar technique
10.1 The principle of a radar interferometer
10.2 Radar interferogram and DEM
10.3 Differential InSAR and deformation measurement
10.4 Multi-temporal coherence image and random change detection
10.5 Spatial de-correlation and ratio coherence technique
10.6 Fringe smoothing filter
Chapter 11 Sub-pixel technology and its applications
11.1 Phase correlation algorithm
11.2 PC scanning for pixel-wise disparity estimation
11.2.1 Disparity estimation by PC scanning
11.2.2 The median shift propagation technique for disparity refinement
11.3 Pixel-wise image co-registration
11.3.1 Basic procedure of pixel-wise image co-registration using PC
11.3.2 An example of pixel-wise image co-registration
11.3.4 Pixel-wise image co-registration-based SFIM pan-sharpen
11.4 Very narrow-baseline stereo matching and 3D data generation
11.4.1 The principle of stereo vision
11.4.2 Wide-baseline vs. narrow-baseline stereo
11.4.3 Narrow-baseline stereo matching using PC
11.4.4 Accuracy assessment and application examples
11.5 Ground motion/deformation detection and estimation
Part II Geographical information systems
Chapter 12 Geographical information systems
12.3 GIS, cartography and thematic mapping
12.4 Standards, inter-operability and metadata
12.5 GIS and the internet
Chapter 13 Data models and structures
13.1 Introducing spatial data in representing geographic features
13.2 How are spatial data different from other digital data?
13.3 Attributes and measurement scales
13.4 Fundamental data structures
13.5.1 Data quantisation and storage
13.5.2 Spatial variability
13.5.3 Representing spatial relationships
13.5.4 The effect of resolution
13.5.5 Representing surface phenomena
13.6.1 Vector data models
13.6.2 Representing logical relationships through geometry and feature definition
13.6.3 Extending the vector data model
13.6.4 Representing surfaces
13.7 Data conversion between models and structures
13.7.1 Vector to raster conversion (rasterisation)
13.7.2 Raster to vector conversion (vectorisation)
Chapter 14 Defining a coordinate space
14.2 Datums and projections
14.2.1 Describing and measuring the earth
14.2.2 Measuring height: The geoid
14.2.3 Coordinate systems
14.2.5 Geometric distortions and projection models
14.2.6 Major map projections
14.2.7 Projection specification
14.3 How coordinate information is stored and accessed
14.4 Selecting appropriate coordinate systems
15.1 Introducing operations on spatial data
15.2 Map algebra concepts
15.2.1 Working with Null data
15.2.2 Logical and conditional processing
15.2.3 Other types of operator
15.3.1 Primary operations
15.4 Neighbourhood operations
15.4.1 Local neighbourhood
15.4.2 Extended neighbourhood
15.5 Vector equivalents to raster map algebra
15.6 Automating GIS functions
Chapter 16 Extracting information from point data: geostatistics
16.2 Understanding the data
16.2.2 Spatial auto-correlation
16.2.4 Underlying trends and natural barriers
16.3.1 Selecting sample size
16.3.2 Interpolation methods
16.3.3 Deterministic interpolators
16.3.4 Stochastic interpolators
Chapter 17 Representing and exploiting surfaces
17.2 Sources and uses of surface data
17.2.1 Digital elevation models
17.2.2 Vector surfaces and objects
17.2.3 Uses of surface data
17.3 Visualising surfaces
17.3.1 Visualising in two dimensions
17.3.2 Visualising in three dimensions
17.4 Extracting surface parameters
17.4.1 Slope: Gradient and aspect
17.4.3 Surface topology: Drainage networks and watersheds
17.4.5 Calculating volume
Chapter 18 Decision support and uncertainty
18.3.1 Criterion uncertainty
18.3.2 Threshold uncertainty
18.3.3 Decision rule uncertainty
18.5 Dealing with uncertainty in GIS-based spatial analysis
18.5.1 Error assessment (criterion uncertainty)
18.5.2 Fuzzy membership (threshold and decision rule uncertainty)
18.5.3 Multi-criteria decision making (decision rule uncertainty)
18.5.4 Error propagation and sensitivity analysis (decision rule uncertainty)
18.5.5 Result validation (decision rule uncertainty)
Chapter 19 Complex problems and multi-criterion evaluation
19.2 Different approaches and models
19.2.1 Knowledge-driven (conceptual)
19.2.2 Data-driven (empirical)
19.2.3 Data-driven (neural network)
19.4 Deriving weighting coefficients
19.4.3 Pairwise comparison
19.5 Multi-criterion combination methods
19.5.1 Boolean logical combination
19.5.2 Index-overlay and algebraic combination
19.5.3 Weights of evidence modelling based on Bayesian probability theory
19.5.4 Belief and Dempster-Shafer Theory
19.5.5 Weighted factors in linear combination (WLC)
19.5.7 Vectorial fuzzy modelling
Part III Remote sensing applications
Chapter 20 Image processing and GIS operation strategy
20.1 General image processing strategy
20.1.1 Preparation of basic working dataset
20.1.3 Image interpretation and map composition
20.2 R emote sensing-based GIS projects: From images to thematic mapping
20.3 An example of thematic mapping based on optimal visualisation and interpretation of multi-spectral satellite imagery
20.3.1 Background information
20.3.2 Image enhancement for visual observation
20.3.3 Data capture and image interpretation
Chapter 21 Thematic teaching case studies in SE Spain
21.1 Thematic information extraction (1): Gypsum natural outcrop mapping and quarry change assessment
21.1.1 Data preparation and general visualisation
21.1.2 Gypsum enhancement and extraction based on spectral analysis
21.1.3 Gypsum quarry changes during 1984–2000
21.1.4 Summary of the case study
21.2 Thematic information extraction (2): Spectral enhancement and mineral mapping of epithermal gold alteration and iron-ore deposits in ferroan dolomite
21.2.1 Image datasets and data preparation
21.2.2 ASTER image processing and analysis for regional prospectivity
21.2.3 ATM image processing and analysis for target extraction
21.2.4 Summary of the case study
21.3 Remote sensing and GIS: Evaluating vegetation and landuse change in the Nijar Basin, SE Spain
21.3.3 Highlighting vegetation
21.3.4 Highlighting plastic greenhouses
21.3.5 Identifying change between different dates of observation
21.3.6 Summary of the case study
21.4 Applied remote sensing and GIS: A combined interpretive tool for regional tectonics, drainage and water resources in the Andarax basin
21.4.2 Geological and hydrological setting
21.4.3 Case study objectives
21.4.4 Landuse and vegetation
21.4.5 Lithological enhancement and discrimination
21.4.6 Structural enhancement and interpretation
21.4.7 Summary of the case study
Chapter 22 Research case studies
22.1 Vegetation change in the Three Parallel Rivers region, Yunnan Province, China
22.1.2 The study area and data
22.1.3 NDVI Difference Red, Green and Intensity (NDVI-D-RGI) composite
22.1.5 Interpretation of regional vegetation changes
22.2 GIS modelling of earthquake damage zones using satellite imagery and digital elevation model (DEM) data
22.2.3 Derivation of input variables
22.2.4 Earthquake damage zone modelling and assessment
22.3 Predicting landslides using fuzzy geohazard mapping: An example from Piemonte, north-west Italy
22.3.3 A holistic GIS-based approach to landslide hazard assessment
22.4 Land surface change detection in a desert area in Algeria using multi-temporal ERS SAR coherence images
22.4.2 Coherence image processing and evaluation
22.4.3 Image visualisation and interpretation for change detection
Chapter 23 Industrial case studies
23.1 Multi-criteria assessment of mineral prospectivity in SE Greenland
23.1.1 Introduction and objectives
23.1.3 Litho-tectonic context – why the project’s concept works
23.1.4 Mineral deposit types evaluated
23.1.6 Multi-criteria spatial modeling
23.2 Water resource exploration in Somalia
23.2.3 P reliminary geological enhancements and target area identification
23.2.4 Discrimination potential aquifer lithologies using ASTER spectral indices
Chapter 24 Concluding remarks
24.2 Geographic information systems
Appendix A Imaging sensor systems and remote sensing satellites
A.1 Multi-spectral sensing
A.2 Broadband multi-spectralsensors
A.2.2 Across-track mechanical scanner
A.2.3 Along-track push-broom scanner
A.3 Thermal sensing and TIRsensors
A.4 Hyperspectral sensors(imaging spectrometers)
A.5 Passive microwave sensors
A.6 Active sensing: SAR imagingsystems
Appendix B Online resources for information, software and data
B.1 Software – proprietary, lowcost and free (shareware)
B.2 Information and technicalinformation on standards, bestpractice, formats, techniquesand various publications
B.3 Data sources including onlinesatellite imagery from majorsuppliers, DEM data plus GISmaps and data of all kinds
Part I References and Further Reading
Part II References and Further Reading
Image Processing and GIS for Remote Sensing
：Techniques and Applications
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