Hyperspectral Data Exploitation

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.