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Proceedings Paper

A Bayesian approach to identification of gaseous effluents in passive LWIR imagery
Author(s): Shawn Higbee; David Messinger; Yolande Tra; Joseph Voelkel; Lawrence Chilton
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Paper Abstract

Typically a regression approach is applied in order to identify the constituents present in a hyperspectral image, and the task of species identification amounts to choosing the best regression model. Common model selection approaches (stepwise and criterion based methods) have well known multiple comparisons problems, and they do not allow the user to control the experimet-wise error rate, or allow the user to include scene-specific knowledge in the inference process. A Bayesian model selection technique called Gibbs Variable Selection (GVS) that better handles these issues is presented and implemented via Markov chain monte carlo (MCMC). GVS can be used to simultaneously conduct inference on the optical path depth and probability of inclusion in a pixel for a each species in a library. This method flexibly accommodates an analyst's prior knowledge of the species present in a scene, as well as mixtures of species of any arbitrary complexity. A series of automated diagnostic measures are developed to monitor convergence of the Markov chains without operator intervention. This method is compared against traditional regression approaches for model selection and results from LWIR data from the Airborne Hyperspectral Imager (AHI) are presented. Finally, the applicability of this identification framework to a variety of scenarios such as persistent surveillance is discussed.

Paper Details

Date Published: 27 April 2009
PDF: 12 pages
Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73341T (27 April 2009); doi: 10.1117/12.818704
Show Author Affiliations
Shawn Higbee, Air Force Institute of Technology (United States)
David Messinger, Rochester Institute of Technology (United States)
Yolande Tra, Rochester Institute of Technology (United States)
Joseph Voelkel, Rochester Institute of Technology (United States)
Lawrence Chilton, Pacific Northwest National Lab. (United States)

Published in SPIE Proceedings Vol. 7334:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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