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Proceedings Paper

Exploiting an atmospheric model for automated invariant material identification in hyperspectral imagery
Author(s): David Slater; Glenn Healey
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Paper Abstract

The measured spectral radiance signature for a material can vary significantly due to atmospheric conditions and scene geometry. We show using a statistical analysis of a comprehensive physical model that the variation in a material's spectral signature lies in a low-dimensional space. The spectral radiance model includes reflected solar and sky radiation as well as path radiance. Signature variability is introduced by effects such as solar occlusion and variation in the concentrations of atmospheric gases aerosols. The MODTRAN 3.5 code was employed for computing radiative transfer aspects of the model. Using the new model, we develop a maximum likelihood algorithm for automatic material identification that is invariant to atmospheric conditions and scene geometry. We demonstrate the algorithm for the identification of exposed and concealed material samples in HYDICE imagery.

Paper Details

Date Published: 2 July 1998
PDF: 12 pages
Proc. SPIE 3372, Algorithms for Multispectral and Hyperspectral Imagery IV, (2 July 1998); doi: 10.1117/12.312609
Show Author Affiliations
David Slater, Univ. of California/Irvine (United States)
Glenn Healey, Univ. of California/Irvine (United States)


Published in SPIE Proceedings Vol. 3372:
Algorithms for Multispectral and Hyperspectral Imagery IV
Sylvia S. Shen; Michael R. Descour, Editor(s)

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