Image processing and GIS for remote sensing : techniques and applications /

Image processing and GIS for remote sensing : techniques and applications /

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Bibliographic Details
Author / Creator:Liu, Jian-Guo, author.
Uniform title:Essential image processing and GIS for remote sensing
Edition:Second edition.
Imprint:Chichester, UK ; Hoboken, NJ : Wiley Blackwell, 2016.
Description:xii, 457 pages ; 26 cm
Language:English
Subject:
Remote sensing.
Geographic information systems.
Image processing.
Geographic information systems.
Image processing.
Remote sensing.
Earth (Planet) -- Surface -- Remote sensing.
Earth (Planet)
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/10518451
Hidden Bibliographic Details
Other authors / contributors:Mason, Philippa J., author.
ISBN:9781118724200
1118724208
Notes:Previously published as: Essential image processing and GIS for remote sensing, 2009.
Includes bibliographical references and index.
Other form:Online version: Liu, Jian-Guo, author. Image processing and GIS for remote sensing. Second edition. Chichester, UK ; Hoboken, NJ : John Wiley & Sons, 2016 9781118724170
Table of Contents:
  • Overview of the book
  • Part I. Image Processing
  • 1. Digital image and display
  • 1.1. What is a digital image?
  • 1.2. Digital image display
  • 1.2.1. Monochromatic display
  • 1.2.2. Tristimulus colour theory and RGB (red, green, blue) colour display
  • 1.2.3. Pseudo-colour display
  • 1.3. Some key points
  • 1.4. Questions
  • 2. Point operations (contrast enhancement)
  • 2.1. Histogram modification and lookup table
  • 2.2. Linear contrast enhancement (LCE)
  • 2.2.1. Derivation of a linear function from two points
  • 2.3. Logarithmic and exponential contrast enhancement
  • 2.3.1. Logarithmic contrast enhancement
  • 2.3.2. Exponential contrast enhancement
  • 2.4. Hisiogram equalisation (HE)
  • 2.5. Histogram matching (HM) and Gaussian stretch
  • 2.6. Balance contrast enhancement technique (BCET)
  • 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
  • 2.9. Questions
  • 3. Algebraic operations (multi-image point operations)
  • 3.1. Image addition
  • 3.2. Image subtraction (differencing)
  • 3.3. Image multiplication
  • 3.4. Image division (ratio)
  • 3.5. Index derivation and supervised enhancement
  • 3.5.1. Vegetation indices
  • 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
  • 3.8. Summary
  • 3.9. Questions
  • 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)
  • 4.3.1. Gaussian filter
  • 4.3.2. K nearest mean filter
  • 4.3.3. Median 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)
  • 4.4.1. Gradient filters
  • 4.4.2. Lapiacian filters
  • 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
  • 4.7. Summary
  • 4.8. Questions
  • 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 THS-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
  • 5.7. Summary
  • 5.8. Questions
  • 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
  • 6.4. Summary
  • 6.5. Questions
  • 7. Principal component analysis
  • 7.1. Principle of the PCA
  • 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
  • 7.7. Remarks
  • 7.8. Questions
  • 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
  • 8.2.3. Seed selection
  • 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
  • 8.4.1. Box classifier
  • 8.4.2. Euclidean distance: Simplified maximum likelihood
  • 8.4.3. 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
  • 8.6. Summary
  • 5.6. Questions
  • 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
  • 9.4. Summary
  • 9.5. Questions
  • 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
  • 10.7. Summary
  • 10.8. Questions
  • 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
  • 11.3.3. Limitations
  • 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
  • 11.6. Summary
  • Part II. Geographical Information Systems
  • 12. Geographical information systems
  • 12.1. Introduction
  • 12.2. Software tools
  • 12.3. GIS, cartography and thematic mapping
  • 12.4. Standards, inter-operability and metadata
  • 12.5. GIS and the internet
  • 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. Raster data
  • 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. Vector data
  • 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)
  • 13.8. Summary
  • 13.9. Questions
  • 14. Defining a coordinate space
  • 14.1. Introduction
  • 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
  • 14.2.4. Datums
  • 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
  • 14.5. Questions
  • 15. Operations
  • 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. Local operations
  • 15.3.1. Primary operations
  • 15.3.2. Unary operations
  • 15.3.3. Binary operations
  • 15.3.4. N-ary operations
  • 15.4. Neighbourhood operations
  • 15.4.1. Local neighbourhood
  • 15.4.2. Extended neighbourhood
  • 15.5. Vector equivalents to raster map algebra
  • 15.5.1. Buffers
  • 15.5.2. Dissolve
  • 15.5.3. Clipping
  • 15.5.4. Intersection
  • 15.6. Automating GIS functions
  • 15.7. Summary
  • 15.8. Questions
  • 16. Extracting information from point data: Geostatistics
  • 16.1. Introduction
  • 16.2. Understanding the data
  • 16.2.1. Histograms
  • 16.2.2. Spatial auto-correlation
  • 16.2.3. Variograms
  • 16.2.4. Underlying trends and natural barriers
  • 16.3. Interpolation
  • 16.3.1. Selecting sample size
  • 16.3.2. Interpolation methods
  • 16.3.3. Deterministic interpolators
  • 16.3.4. Stochastic interpolators
  • 16.4. Summary
  • 16.5. Questions
  • 17. Representing and exploiting surfaces
  • 17.1. Introduction
  • 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
  • 17.4.2. Curvature
  • 17.4.3. Surface topology: Drainage networks and watersheds
  • 17.4.4. Viewshed
  • 17.4.5. Calculating volume
  • 17.5. Summary
  • 17.6. Questions
  • 18. Decision support and uncertainly
  • 18.1. Introduction
  • 18.2. Decision support
  • 18.3. Uncertainty
  • 18.3.1. Criterion uncertainty
  • 18.3.2. Threshold uncertainty
  • 18.3.3. Decision rule uncertainty
  • 18.4. Risk and hazard
  • 18.5. Dealing with uncertainty in GIS-based spatial analysis
  • 18.5.1. Error assessment (criterion uncertainty)
  • 18.5.1. Fuzzy membership (threshold and decision rule uncertainty)
  • 18.5.2. Multi-criteria decision making (decision rule uncertainty)
  • 18.5.3. Error propagation and sensitivity analysis (decision rule uncertainty)
  • 18.5.4. Result validation (decision rule uncertainty)
  • 18.6. Summary
  • 18.7. Questions
  • 19. Complex problems and multi-criterion evaluation
  • 19.1. Introduction
  • 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)
  • 19.3. Evaluation criteria
  • 19.4. Deriving weighting coefficients
  • 19.4.1. Rating
  • 19.4.2. Ranking
  • 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)
  • 19.5.6. Fuzzy logic
  • 19.5.7. Vectorial fuzzy modelling
  • 19.6. Summary
  • 19.7. Questions
  • Part III. Remote Sensing applications
  • 20. Image processing and GIS operation strategy
  • 20.1. General image processing strategy
  • 20.1.1. Preparation of basic working dataset
  • 20.1.2. Image processing
  • 20.1.3. Image interpretation and map composition
  • 20.2. Remote 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
  • 20.3.4. Map composition
  • 20.4. Summary
  • 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
  • 21.1.5. Questions
  • 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
  • 21.2.5. Questions
  • 21.3. Remote sensing and GIS: Evaluating vegetation and landuse change in the Nijar Basin, SE Spain
  • 21.3.1. Introduction
  • 21.3.2. Data preparation
  • 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
  • 21.3.7. Questions
  • 21.3.8. References
  • 21.4. Applied remote sensing and GIS: A combined interpretive tool for regional tectonics, drainage and water resources in the Andarax basin
  • 21.4.1. Introduction
  • 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
  • 21.4.8. Questions
  • 21.4.9. References
  • 22. Research case studies
  • 22.1. Vegetation change in the Three Parallel Rivers region, Yunnan Province, China
  • 22.1.1. Introduction
  • 22.1.2. The study area and data
  • 22.1.3. NDVI Difference Red, Green and Intensity (NDV1-D-RGI) composite
  • 22.1.4. Data processing
  • 22.1.5. Interpretation of regional vegetation changes
  • 22.1.6. Summary
  • 22.1.7. References
  • 22.2. GTS modelling of earthquake damage zones using satellite imagery and digital elevation model (DEM) data
  • 22.2.1. Introduction
  • 22.2.2. The models
  • 22.2.3. Derivation of input variables
  • 22.2.4. Earthquake damage zone modelling and assessment
  • 22.2.5. Summary
  • 22.2.6. References
  • 22.3. Predicting landslides using fuzzy geohazard mapping: An example from Piemonte, north-west Italy
  • 22.3.1. Introduction
  • 22.3.2. The study area
  • 22.3.3. A holistic GIS-based approach to landslide hazard assessment
  • 22.3.4. Summary
  • 22.3.5. Questions
  • 22.3.6. References
  • 22.4. Land surface change detection in a desert area in Algeria using multi-temporal ERS SAR coherence images
  • 22.4.1. The study area
  • 22.4.2. Coherence image processing and evaluation
  • 22.4.3. Image visualisation and interpretation for change detection
  • 22.4.4. Summary
  • 22.4.5. References
  • 23. Industrial case studies
  • 23.1. Multi-criteria assessment of mineral prospectivity in SE Greenland
  • 23.1.1. Introduction and objectives
  • 23.1.2. Area description
  • 23.1.3. Litho-tectonic context - why the project's concept works
  • 23.1.4. Mineral deposit types evaluated
  • 23.1.5. Data preparation
  • 23.1.6. Multi-criteria spatial modeling
  • 23.1.7. Summary
  • 23.1.8. Questions
  • 23.1.9. Acknowledgements
  • 23.1.10. References
  • 23.2. Water resource exploration in Somalia
  • 23.2.1. Introduction
  • 23.2.2. Data preparation
  • 23.2.3. Preliminary geological enhancements and target area identification
  • 23.2.4. Discrimination potential aquiler lithologies using ASTER spectral indices
  • 23.2.5. Summary
  • 23.2.6. Questions
  • 23.2.7. References
  • Part IV. Summary
  • 24. Concluding remarks
  • 24.1. Image processing
  • 24.2. Geographic Information Systems
  • 24.3. Final remarks
  • Appendix A. Imaging sensor systems and remote sensing satellites
  • A.1. Multi-spectral sensing
  • A.2. Broadband multi-spectral sensors
  • A.2.1. Digital camera
  • A.2.2. Across-track mechanical scanner
  • A.2.3. Along-track push-broom scanner
  • A.3. Thermal sensing and TIR sensors
  • A.4. Hyperspectral sensors (imaging spectrometers)
  • A.5. Passive microwave sensors
  • A.6. Active sensing: SAR imaging systems
  • Appendix B. Online resources for information, software and data
  • B.1. Software - proprietary, low cost and free (shareware)
  • B.2. Information and technical information on standards, best practice, formals, techniques and various publications
  • B.3. Data sources including online satellite imagery from major suppliers, DEM data plus GIS maps and data of all kinds
  • References
  • Index
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