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Hyperspectral Data Exploitation: Theory and Applications Edited by Chein-I Chang Table of Contents 1. Chapter 1: Overview Chein-I Chang Remote Sensing Signal and Image Processing Laboratory University of Maryland, Baltimore County, Baltimore, MD, USA PRAT I: TUTORALS 2. Chapter 2: Hyperspectral Imaging Systems John P. Kerekes and John R. Schott Chester F. Carlson Center for Imaging Science Rochester Institute of Technology, Rochester, N.Y., USA 3. Chapter 3: Information-Processed Matched Filters for Hyperspectral Target Detection and Classification Chein-I Chang Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland, Baltimore County, Baltimore, MD, USA PRAT II: THEORY 4. Chapter 4: An Optical Real-Time Adaptive Spectral Identification System (ORASIS) Jeffery H. Bowles and David B. Gillis Remote Sensing Division Naval Research Laboratory, Washington DC, USA 5. Chapter 5: Stochastic Mixture Modeling Michael T. Eismann1 and David W. J. Stein2 1AFRL's Sensors Directorate, Electro Optical Technology Division Electro Optical Targeting Branch, Wright-Patterson AFB OH, USA 2MIT Lincoln Laboratory, Boston, MA, USA 6. Chapter 6: Unmixing Hyperspectral Data: Independent and Dependent Component Analysis Jose M.P. Nascimento1 and Jose M.B. Dias2 1Instituto Superior De Engenharia de Lisboa 2Instituto de Telecomunications, Lisbon, Portugal 7. Chapter 7: Maximum Volume Transform For Endmember Spectra Determination Michael E. Winter Hawaii Institute of Geophysics and Planetology University of Hawaii, Honolulu, HI, USA 8. Chapter 8: Hyperspectral Data Representation X. Jia1 and John A. Richards2 1Australian Defense Force Academy, Australia 2The Australia National University, Australia 9. Chapter 9: Optimal Band Selection and Utility Evaluation for Spectral Systems Sylvia S. Shen The Aerospace Corporation, USA 10. Chapter 10: Feature Reduction for Classification Purpose Sebastiano B. Serpico, Gabriele Moser and Andrea F. Cattoni Department of Biophysics and Electronic Engineering University of Genoa, Genoa, Italy 11. Chapter 11: Semi-supervised Support Vector Machines for Classification of Hyperspectral Remote Sensing Images Lorenzo Bruzzone, Mingmin Chi, Mattia Marconcini Department of Information and Communication Technolog University of Trento, Italy PRAT III: APPLICATIONS 12. Chapter 12: Decision Fusion for Hyperspectral Classification Mathieu Fauvel*?, Jocelyn Chanussot*, and Jon Atli Benediktsson? *Laboratoire des Images et des Signaux ? LIS-GIPSA/INPG, France ?Department of Electrical and Computer Engineering, University of Iceland, Iceland 13. Chapter 13: Morphological Hyperspectral Image Classification: A Parallel Processing Perspective Antonio J. Plaza Computer Science Department, University of Extremadura, Avda. de la Universidad s/n, 10071 Caceres, SPAIN 14. Chapter 14: 3D Wavelet-Based Compression of Hyperspectral Imagery James E. Fowler and Justin T. Rucker Department of Electrical and Computer Engineering, GeoResources Institute Mississippi State University, USA
Library of Congress Subject Headings for this publication:
Remote sensing.
Multispectral photography.
Image processing -- Digital techniques.