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Proceedings Paper

A method for quantification of gas plumes in thermal hyperspectral imagery
Author(s): David W. Messinger
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Paper Abstract

Several commercial and environmental applications require the detection and quantification of gaseous plumes from airborne platforms. Unlike active LIDAR imaging in a DIAL system, the signal received by a passive sensor depends not only on the gas concentration pathlength, but the temperature contrast between the gas column and the background as well. Further complicating the problem, the at-sensor radiance is a function of a non-linear combination of the gas concentration and temperature, both inherently unknown. A method is presented to estimate the gas concentration pathlength and temperature from LWIR Hyperspectral Imagery (HSI) without any assumptions about the gas properties or background radiance. A non-linear model is fit to the data using a Levenberg-Marquardt fitting procedure. This technique requires only a priori knowledge of the gas species present in the pixel of interest to reduce the complexity of the model. The resulting concentration pathlength and temperature are reported on a per-pixel basis. Results are shown for application to synthetic imagery created with the DIRSIG simulation. Concentration pathlength results are promising for a gas with strong, moderately broad features (Freon) but less so for a gas with weaker, narrow features (NH3). In neither case is the solution to the gas temperature satisfactory. This is further demonstrated through examination of the residual space in which the minimization is performed where it is shown that a unique minimum is not present in the space.

Paper Details

Date Published: 1 June 2005
PDF: 11 pages
Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); doi: 10.1117/12.603585
Show Author Affiliations
David W. Messinger, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 5806:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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