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Sensors 2007, 7(6), 905-920; doi:10.3390/s7060905

Nonlinear Bayesian Algorithms for Gas Plume Detection and Estimation from Hyper-spectral Thermal Image Data

* ,
PO Box 999, Pacific Northwest National Laboratory, Richland, WA 99352, USA
* Author to whom correspondence should be addressed.
Received: 12 April 2007 / Accepted: 6 June 2007 / Published: 7 June 2007
(This article belongs to the Special Issue Remote Sensing of Natural Resources and the Environment)
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This paper presents a nonlinear Bayesian regression algorithm for detecting and estimating gas plume content from hyper-spectral data. Remote sensing data, by its very nature, is collected under less controlled conditions than laboratory data. As a result, the physics-based model that is used to describe the relationship between the observed remote- sensing spectra, and the terrestrial (or atmospheric) parameters that are estimated is typically littered with many unknown ”nuisance” parameters. Bayesian methods are well-suited for this context as they automatically incorporate the uncertainties associated with all nuisance parameters into the error estimates of the parameters of interest. The nonlinear Bayesian re- gression methodology is illustrated on simulated data from a three-layer model for longwave infrared (LWIR) measurements from a passive instrument. The generated LWIR scenes con- tain plumes of varying intensities, and this allows estimation uncertainty and probability of detection to be quantified. The results show that this approach should permit more accurate estimation as well as a more reasonable description of estimate uncertainty. Specifically, the methodology produces a standard error that is more realistic than that produced by matched filter estimation.
Keywords: plumes; bayesian; regression; MCMC; hyperspectral; LWIR; uncertainty plumes; bayesian; regression; MCMC; hyperspectral; LWIR; uncertainty
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Heasler, P.; Posse, C.; Hylden, J.; Anderson, K. Nonlinear Bayesian Algorithms for Gas Plume Detection and Estimation from Hyper-spectral Thermal Image Data. Sensors 2007, 7, 905-920.

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