Hyperspectral Data Exploitation (eBook, PDF)

Theory and Applications
Redaktion: Chang, Chein-I

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 440
• Erscheinungstermin: 22. August 2007
• Englisch
• ISBN-13: 9780470124611
• Artikelnr.: 37291007

Autorenporträt

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.

Inhaltsangabe

Preface.
Contributors.
1. Overview (Chein-I Chang).
I TUTORALS.
2. Hyperspectral Imaging Systems (John P. Kerekes and John R. Schott).
3. Information-Processed Matched Filters for Hyperspectral Target 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 Dependent Component
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 Spectral Systems
(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 Classification of
Hyperspectral Remote Sensing Images (Lorenzo Bruzzone, Mingmin Chi, and
Mattia Marconcini).
III APPLICATIONS.
12. Decision Fusion for Hyperspectral Classification (Mathieu Fauvel,
Jocelyn Chanussot, and Jon Atli Benediktsson)
13. Morphological Hyperspectral Image Classification: A Parallel Processing
Perspective (Antonio J. Plaza).
14. Three-Dimensional Wavelet-Based Compression of Hyperspectral Imagery
(James E. Fowler and Justin T. Rucker).
Index.