Hyperspectral Imaging Remote Sensing
Physics, Sensors, and Algorithms
Published on 5. October 2016
978-1-316-02859-9 (ISBN)
Description
A practical and self-contained guide to the principles, techniques, models and tools of imaging spectroscopy. Bringing together material from essential physics and digital signal processing, it covers key topics such as sensor design and calibration, atmospheric inversion and model techniques, and processing and exploitation algorithms. Readers will learn how to apply the main algorithms to practical problems, how to choose the best algorithm for a particular application, and how to process and interpret hyperspectral imaging data. A wealth of additional materials accompany the book online, including example projects and data for students, and problem solutions and viewgraphs for instructors. This is an essential text for senior undergraduate and graduate students looking to learn the fundamentals of imaging spectroscopy, and an invaluable reference for scientists and engineers working in the field.
Reviews / Votes
'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 'An extraordinarily comprehensive treatment of hyperspectral remote sensing by three of the field's noted authorities. An indispensable reference for those new to the field and for the seasoned professional.' Ronald G. Resmini, George Mason University, Virginia 'The authors have offered a comprehensive and up-to-date treatment of hyperspectral imaging modalities. A wide readership, including scientists and graduate students involved with spectral imaging modalities, could benefit from this book.' Axel Mainzer Koenig, Optics and PhotonicsMore details
Language
English
Place of publication
Cambridge
United Kingdom
Publishing group
Cambridge University Press
Target group
Professional and scholarly
Illustrations
34 Tables, black and white; 16 Plates, color; 71 Halftones, black and white; 320 Line drawings, black and white
File size
23159,82 MB
ISBN-13
978-1-316-02859-9 (9781316028599)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Dimitris G. Manolakis | Ronald B. Lockwood | Thomas W. Cooley
Hyperspectral Imaging Remote Sensing
Physics, Sensors, and Algorithms
Book
10/2016
Cambridge University Press
€168.30
Shipment within 15-20 days
Persons
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.
Content
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.
