Hyperspectral Data Exploitation: Theory and Applications / Edition 1

Hyperspectral Data Exploitation: Theory and Applications / Edition 1

by Chein-I Chang
     
 

ISBN-10: 0471746975

ISBN-13: 9780471746973

Pub. Date: 04/06/2007

Publisher: Wiley

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,

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:
6.50(w) x 9.49(h) x 1.03(d)

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.

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