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

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 remote sensing 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. New additions to the second 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
• Many new end-of-chapter exercises and programming projects

The code is available for upload from the author's website athttp://mcanty.homepage.t-online.de/software.html

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 the ideal resource for classroom use or self study.