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Proceedings Paper

Impact of background and atmospheric variability on infrared hyperspectral chemical detection sensitivity
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Paper Abstract

Chemical detection using infrared hyperspectral imaging systems often is limited by the effects of variability of the scene background emissivity spectra and temperature. Additionally, the atmospheric up-welling and down-welling radiance and transmittance are difficult to estimate from the hyperspectral image data, and may vary across the image. In combination, these background variability effects are referred to as "clutter." A study has been undertaken at Pacific Northwest National Laboratory to determine the relative impact of atmospheric variability and background variability on the detection of trace chemical vapors. This study has analyzed Atmospheric Emitted Radiance Interferometer data to estimate fluctuations in atmospheric constituents. To allow separation of the effects of background and atmospheric variability, hyperspectral data was synthesized using large sets of simulated atmospheric spectra, measured background emissivity spectra, and measured high-resolution gas absorbance spectra. The atmosphere was simulated using FASCODE in which the constituent gas concentrations and temperatures were varied. These spectral sets were combined synthetically using a physics model to realize a statistical synthetic scene with a plume present in a portion of the image. Noise was added to the image with the level determined by a numerical model of the hyperspectral imaging instrument. The chemical detection performance was determined by applying a matched-filter estimator to both the on-plume and off-plume regions. The detected levels in the off-plume region were then used to determine the noise equivalent concentration path length (NECL), a measure of the chemical detection sensitivity. The NECL was estimated for numerous gases and for a variety of background and atmospheric conditions to determine the relative impact of instrument noise, background variability, and atmospheric variability.

Paper Details

Date Published: 23 September 2003
PDF: 12 pages
Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, (23 September 2003); doi: 10.1117/12.488931
Show Author Affiliations
David M. Sheen, Pacific Northwest National Lab. (United States)
Neal B. Gallagher, Eigenvector Research, Inc. (United States)
Steven W. Sharpe, Pacific Northwest National Lab. (United States)
Kevin K. Anderson, Pacific Northwest National Lab. (United States)
John F. Schultz, Pacific Northwest National Lab. (United States)

Published in SPIE Proceedings Vol. 5093:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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