Spectral imaging analysis models

Keshav Dev Singh

ISBN 10: 3659250325 / ISBN 13: 9783659250323

Published by LAP LAMBERT Academic Publishing Okt 2012, 2012

About this Item

Neuware -Hyperspectral Imagery combines imaging and spectroscopy in single systems which often include large data sets and require new processing methods. The spatial resolution of most space borne Hyperspectral (HSI) sensors is much coarser than the size of the objects being observed. Therefore the measured spectral signature is a mixture of the signatures of the objects in the field of view (FOV) of the sensor. One of the tedious and time consuming tasks related to hyperspectral data analysis is the identification of library candidates. The high spectral resolution can be effectively used to decompose measured spectra into its constituents by way of spectral unmixing. Spectral Unmixing is the process by which the measured spectrum is decomposed into a collection of constituent spectra (or endmembers), and a set of corresponding fractions (or abundances). Thus spectral unmixing allows us to detect and classify sub-pixel target objects by their contribution to the measured spectral signature. The application area includes Agriculture, Forestry, Geology, Hydrology, Sea ice and Water, Environment, Land cover, Mapping, Ocean and Coastal, Military Operations, Geo-explorations, and Bio-sciences.Books on Demand GmbH, Überseering 33, 22297 Hamburg 264 pp. Englisch.

Seller Inventory # 9783659250323

Bibliographic Details

• Title: Spectral imaging analysis models
• Publisher: LAP LAMBERT Academic Publishing Okt 2012
• Publication Date: 2012
• Language: English
• ISBN 10: 3659250325
• ISBN 13: 9783659250323
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 16D millimeters 220 millimeters width by 150 millimeters height by 16 millimeters depth
• Weight: 411 grams

About this title

Synopsis

Hyperspectral Imagery combines imaging and spectroscopy in single systems which often include large data sets and require new processing methods. The spatial resolution of most space borne Hyperspectral (HSI) sensors is much coarser than the size of the objects being observed. Therefore the measured spectral signature is a mixture of the signatures of the objects in the field of view (FOV) of the sensor. One of the tedious and time consuming tasks related to hyperspectral data analysis is the identification of library candidates. The high spectral resolution can be effectively used to decompose measured spectra into its constituents by way of spectral unmixing. Spectral Unmixing is the process by which the measured spectrum is decomposed into a collection of constituent spectra (or endmembers), and a set of corresponding fractions (or abundances). Thus spectral unmixing allows us to detect and classify sub-pixel target objects by their contribution to the measured spectral signature. The application area includes Agriculture, Forestry, Geology, Hydrology, Sea ice and Water, Environment, Land cover, Mapping, Ocean and Coastal, Military Operations, Geo-explorations, and Bio-sciences.

About the Author

He is a Doctorate’ student at Indian Institute of Technology Bombay, Mumbai. He received his graduation and post graduation both in Physics from University of Delhi. He completed his Master of Technology in Engineering Physics. He is a gold medalist recipient in Engineering Physics. His area of specialization includes Hyperspectral Remote Sensing.