Exploiting an atmospheric model for automated invariant material identification in hyperspectral imagery

David Slater; Glenn Healey

doi:10.1117/12.312609

2 July 1998 Exploiting an atmospheric model for automated invariant material identification in hyperspectral imagery

David Slater, Glenn Healey

Proceedings Volume 3372, Algorithms for Multispectral and Hyperspectral Imagery IV; (1998) https://doi.org/10.1117/12.312609
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

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.

Citation Download Citation

David Slater and Glenn Healey "Exploiting an atmospheric model for automated invariant material identification in hyperspectral imagery", Proc. SPIE 3372, Algorithms for Multispectral and Hyperspectral Imagery IV, (2 July 1998); https://doi.org/10.1117/12.312609

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