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Proceedings Paper

Multivariate curve resolution for the analysis of remotely-sensed thermal infrared hyperspectral images
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Paper Abstract

While hyperspectral imaging systems are increasingly used in remote sensing and offer enhanced scene characterization relative to univariate and multispectral technologies, it has proven difficult in practice to extract all of the useful information from these systems due to overwhelming data volume, confounding atmospheric effects, and the limited a priori knowledge regarding the scene. The need exists for the ability to perform rapid and comprehensive data exploitation of remotely sensed hyperspectral imagery. To address this need, this paper describes the application of a fast and rigorous multivariate curve resolution (MCR) algorithm to remotely sensed thermal infrared hyperspectral images. Employing minimal a priori knowledge, notably non-negativity constraints on the extracted endmember profiles and a constant abundance constraint for the atmospheric upwelling component, it is demonstrated that MCR can successfully compensate thermal infrared hyperspectral images for atmospheric upwelling and, thereby, transmittance effects. We take a semi-synthetic approach to obtaining image data containing gas plumes by adding emission gas signals onto real hyperspectral images. MCR can accurately estimate the relative spectral absorption coefficients and thermal contrast distribution of an ammonia gas plume component added near the minimum detectable quantity.

Paper Details

Date Published: 15 October 2004
PDF: 14 pages
Proc. SPIE 5546, Imaging Spectrometry X, (15 October 2004); doi: 10.1117/12.559604
Show Author Affiliations
Chris L. Stork, Sandia National Labs. (United States)
Michael R. Keenan, Sandia National Labs. (United States)
David M. Haaland, Sandia National Labs. (United States)

Published in SPIE Proceedings Vol. 5546:
Imaging Spectrometry X
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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