Spectral imaging analysis models

Keshav Dev Singh

ISBN 10: 3659250325 / ISBN 13: 9783659250323

Editore: LAP LAMBERT Academic Publishing Okt 2012, 2012

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This item is printed on demand - it takes 3-4 days longer - 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. 264 pp. Englisch.

Codice articolo 9783659250323

Dati bibliografici

• Titolo: Spectral imaging analysis models
• Editore: LAP LAMBERT Academic Publishing Okt 2012
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 3659250325
• ISBN 13: 9783659250323
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 220 L x 150 A x 16 P (millimetri) 220 millimetri di larghezza per 150 millimetri di altezza per 16 millimetri di profondità
• Peso: 411 grammi

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

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.