Hyperspectral Imaging Remote Sensing

Dimitris G. Manolakis is a senior member of technical staff at the Lincoln Laboratory, Massachusetts Institute of Technology. He is the co-author of Applied Digital Signal Processing (Cambridge, 2011), and has taught at various institutions including Northeastern University, Boston, Boston College, Massachusetts, and Worcester Polytechnic Institute, Massachusetts. He is an IEEE Fellow, and in 2013 he received the IEEE Signal Processing Society Education Award. Ronald B. Lockwood is a member of technical staff at the Lincoln Laboratory, Massachusetts Institute of Technology. He previously worked at the Air Force Research Laboratory where he developed imaging spectrometers for both space-based and air-borne applications. He has also developed vicarious calibration techniques in collaboration with colleagues at the University of Arizona and the NASA Goddard Space Flight Center. Thomas W. Cooley is the Senior Scientist for Space Situational Awareness at the US Air Force Research Laboratory, and has made significant contributions to the fields of atmospheric compensation and spectral data analysis. He developed the ARTEMIS sensor program, which was successfully launched in 2009, and has published over 70 research papers.

'The authors have done a masterful job of integrating and presenting the diverse subjects that form the foundation of the field of hyperspectral imaging and applications. This comprehensive textbook will clearly become one of the standard references for all who wish to learn about both fundamentals and advanced applications in this important field.' Charles Bachmann, Rochester Institute of Technology, New York

• 1. Introduction; 2. The remote sensing environment; 3. Spectral properties of materials; 4. Imaging spectrometers; 5. Imaging spectrometer characterization and data calibration; 6. Radiative transfer and atmospheric compensation; 7. Statistical models for spectral data; 8. Linear spectral transformations; 9. Spectral mixture analysis; 10. Signal detection theory; 11. Hyperspectral data exploitation; Appendix. Introduction to Gaussian optics.