Image Analysis, Classification, and Change Detection in Remote Sensing

Image Analysis, Classification, and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL, Second Edition

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Features

    • Introduces the required mathematical and statistical background—clearly and concisely
    • Provides in-depth coverage of kernel methods for nonlinear data analysis, including supervised classification with Support Vector Machines
    • Supplies thorough treatment of multivariate change detection along with efficient software implementations
    • Keeps pace with the latest versions of the ENVI software environment—IDL 7.0, ENVI 4.4 and beyond
    • Contains almost twice as many exercises and programming projects at the end of each chapter than the previous edition
    • Includes access to additional IDL extensions to ENVI on the author’s website—along with updated versions of previous programs

    A solutions manual is available upon qualifying course adoption.

    Summary

    Demonstrating the breadth and depth of growth in the field since the publication of the popular first edition, Image Analysis, Classification and Change Detection in Remote Sensing, with Algorithms for ENVI/IDL, Second Edition has been updated and expanded to keep pace with the latest versions of the ENVI software environment. Effectively interweaving theory, algorithms, and computer codes, the text supplies an accessible introduction to the techniques used in the processing of remotely sensed imagery.

    This significantly expanded edition presents numerous image analysis examples and algorithms, all illustrated in the array-oriented language IDL—allowing readers to plug the illustrations and applications covered in the text directly into the ENVI system—in a completely transparent fashion. Revised chapters on image arrays, linear algebra, and statistics convey the required foundation, while updated chapters detail kernel methods for principal component analysis, kernel-based clustering, and classification with support vector machines.

    Additions to this edition include:

    • An introduction to mutual information and entropy
    • Algorithms and code for image segmentation
    • In-depth treatment of ensemble classification (adaptive boosting )
    • Improved IDL code for all ENVI extensions, with routines that can take advantage of the parallel computational power of modern graphics processors
    • Code that runs on all versions of the ENVI/IDL software environment from ENVI 4.1 up to the present—available on the author's website
    • Many new end-of-chapter exercises and programming projects

    With its numerous programming examples in IDL and many applications supporting ENVI, such as data fusion, statistical change detection, clustering and supervised classification with neural networks—all available as downloadable source code—this self-contained text is ideal for classroom use or self study.

    Table of Contents

    Images, Arrays, and Matrices
    Multispectral Satellite Images
    Algebra of Vectors and Matrices
    Eigenvalues and Eigenvectors
    Singular Value Decomposition
    Vector Derivatives
    Finding Minima and Maxima

    Image Statistics
    Random Variables
    Random Vectors
    Parameter Estimation
    Hypothesis Testing and Sample Distribution Functions
    Conditional Probabilities, Bayes’ Theorem, and Classification
    Ordinary Linear Regression
    Entropy and Information

    Transformations
    Discrete Fourier Transform
    Discrete Wavelet Transform
    Principal Components
    Minimum Noise Fraction
    Spatial Correlation

    Filters, Kernels, and Fields
    Convolution Theorem
    Linear Filters
    Wavelets and Filter Banks
    Kernel Methods
    Gibbs–Markov Random Fields

    Image Enhancement and Correction
    Lookup Tables and Histogram Functions
    Filtering and Feature Extraction
    Panchromatic Sharpening
    Topographic Correction
    Image–Image Registration

    Supervised Classification: Part 1
    Maximum a Posteriori Probability
    Training Data and Separability
    Maximum Likelihood Classification
    Gaussian Kernel Classification
    Neural Networks
    Support Vector Machines

    Supervised Classification: Part 2
    Postprocessing
    Evaluation and Comparison of Classification Accuracy
    Adaptive Boosting
    Hyperspectral Analysis

    Unsupervised Classification
    Simple Cost Functions
    Algorithms That Minimize the Simple Cost Functions
    Gaussian Mixture Clustering
    Including Spatial Information
    Benchmark
    Kohonen Self-Organizing Map
    Image Segmentation

    Change Detection
    Algebraic Methods
    Postclassification Comparison
    Principal Components Analysis
    Multivariate Alteration Detection
    Decision Thresholds and Unsupervised Classification of Changes
    Radiometric Normalization

    Appendix A: Mathematical Tools
    Cholesky Decomposition
    Vector and Inner Product Spaces
    Least Squares Procedures

    Appendix B: Efficient Neural Network Training Algorithms
    Hessian Matrix
    Scaled Conjugate Gradient Training
    Kalman Filter Training
    A Neural Network Classifier with Hybrid Training

    Appendix C: ENVI Extensions in IDL
    Installation
    Extensions

    Appendix D: Mathematical Notation

    References

    Index

    Downloads Updates

    Resource OS Platform Updated Description Instructions
    Cross Platform January 18, 2012 Author's Web site click on http://mcanty.homepage.t-online.de/software.html

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