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

1. Chapter 1: Overview (Chein-I Chang) PART I: TUTORALS 2. Chapter 2.
Hyperspectral Imaging Systems (John P. Kerekes and John R. Schott) Chapter
3. Information-Processed Matched Filters for Hyperspectral Target Detection
and Classification (Chein-I Chang) PART II: THEORY Chapter 4. An Optical
Real-Time Adaptive Spectral Identification System (ORASIS) (Jeffery H.
Bowles and David B. Gillis) Chapter 5: Stochastic Mixture Modeling (Michael
T. Eismann1 and David W. J. Stein) Chapter 6. Unmixing Hyperspectral Data:
Independent and Dependent Component Analysis (Jose M.P. Nascimento1 and
Jose M.B. Dias) Chapter 7. Maximum Volume Transform For Endmember Spectra
Determination (Michael E. Winter) Chapter 8. Hyperspectral Data
Representation (X. Jia1 and John A. Richards) Chapter 9. Optimal Band
Selection and Utility Evaluation for Spectral Systems (Sylvia S. Shen)
Chapter 10. Feature Reduction for Classification Purpose (Sebastiano B.
Serpico, Gabriele Moser and Andrea F. Cattoni) Chapter 11. Semi-supervised
Support Vector Machines for Classification of Hyperspectral Remote Sensing
Images (Lorenzo Bruzzone, Mingmin Chi and Mattia Marconcini) PART III:
APPLICATIONS Chapter 12. Decision Fusion for Hyperspectral Classification
(Mathieu Fauvel, Jocelyn Chanussot and Jon Atli Benediktsson) Chapter 13.
Morphological Hyperspectral Image Classification: A Parallel Processing
Perspective (Antonio J. Plaza) Chapter 14. 3D Wavelet-Based Compression of
Hyperspectral Imagery (James E. Fowler and Justin T. Rucker)