Hyperspectral Imaging Remote Sensing
Physics, Sensors, and Algorithms

Understand the seminal principles, current techniques, and tools of imaging spectroscopy with this self-contained introductory guide.

Dimitris G. Manolakis (Author), Ronald B. Lockwood (Author), Thomas W. Cooley (Author)

9781107083660, Cambridge University Press

Hardback, published 20 October 2016

706 pages, 391 b/w illus. 16 colour illus. 34 tables
25.3 x 18 x 3.5 cm, 1.5 kg

'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 Photonics

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.

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.

Subject Areas: Communications engineering / telecommunications [TJK], Electronics & communications engineering [TJ], Technology, engineering, agriculture [T], Geographical information systems [GIS & remote sensing RGW], Geography [RG], Earth sciences, geography, environment, planning [R]