Hyperspectral Data Exploitation: Theory and Applications / Edition 1

by Chein-I Chang

Overview

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.

Product Details

ISBN-13:	9780471746973
Publisher:	Wiley
Publication date:	04/06/2007
Pages:	440
Product dimensions:	6.40(w) x 9.55(h) x 1.00(d)

About the Author

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.

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Table of Contents

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.