Hyperspectral Data Exploitation: Theory and Applications

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.