Hyperspectral Data Exploitation
Theory and Applications
Published on 22. August 2007
456 pages
978-0-470-12461-1 (ISBN)
Description
Authored by a panel of experts in the field, this book focuses on hyperspectral image analysis, systems, and applications. With discussion of application-based projects and case studies, this professional reference will bring you up-to-date on this pervasive technology, wether you are working in the military and defense fields, or in remote sensing technology, geoscience, or agriculture.
More details
Other editions
Additional editions
Chein-I Chang
Hyperspectral Data Exploitation - Theory and ations
Software
06/2007
Wiley
€185.05
Shipment within 10-20 days
Book
05/2007
Wiley
€193.50
Article not available at the moment
Person
Chein-I Chang, PHD, is Professor in the Department of Computer Sciences and Electrical Engineering at the University of Maryland, Baltimore County, where he directs the Remote Sensing Signal and Image Processing Laboratory. Dr. Chang is a Fellow of SPIE, the International Society for Optical Engineering, for his achievements in hyperspectral image processing. He is Associate Editor of the IEEE Transactions on Geoscience and Remote Sensing and the author of Hyperspectral Imaging: Techniques for Spectral Detection and Classification.
Content
Preface.
Contributors.
1. Overview (Chein-I Chang).
I TUTORALS.
2. Hyperspectral Imaging Systems (John P. Kerekes and JohnR. Schott).
3. Information-Processed Matched Filters for HyperspectralTarget Detection and Classification (Chein-I Chang).
II THEORY.
4. An Optical Real-Time Adaptive Spectral Identification System(ORASIS) (Jeffery H. Bowles and David B. Gillis).
5. Stochastic Mixture Modeling (Michael T. Eismann1 and David W.J. Stein).
6. Unmixing Hyperspectral Data: Independent and DependentComponent Analysis (Jose M.P. Nascimento1 and Jose M.B. Dias).
7. Maximum Volume Transform For Endmember Spectra Determination(Michael E. Winter).
8. Hyperspectral Data Representation (Xiuping Jia and John A.Richards).
9. Optimal Band Selection and Utility Evaluation for SpectralSystems (Sylvia S. Shen).
10. Feature Reduction for Classification Purpose (Sebastiano B.Serpico, Gabriele Moser, and Andrea F. Cattoni).
11. Semi-supervised Support Vector Machines for Classificationof Hyperspectral Remote Sensing Images (Lorenzo Bruzzone, MingminChi, and Mattia Marconcini).
III APPLICATIONS.
12. Decision Fusion for Hyperspectral Classification (MathieuFauvel, Jocelyn Chanussot, and Jon Atli Benediktsson)
13. Morphological Hyperspectral Image Classification: A ParallelProcessing Perspective (Antonio J. Plaza).
14. Three-Dimensional Wavelet-Based Compression of HyperspectralImagery (James E. Fowler and Justin T. Rucker).
Index.
