Image Analysis, Classification, and Change Detection in Remote Sensing

An introduction to the processing of remote sensing imagery that includes coverage of various versions of the ENVI software environment. It covers support vector machines and other kernel-based methods. It also includes coverage of basic Fourier, wavelet, principal components and minimum noise fraction transformations.

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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.