skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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

Abstract

This paper presents a nonlinear Bayesian regression algorithm for the purpose of 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 remotesensing spectra, and the terrestrial (or atmospheric) parameters that we desire to estimate, is typically littered with many unknown "nuisance" parameters (parameters that we are not interested in estimating, but also appear in the model). 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 regression methodology is illustrated on realistic simulated data from a three-layer model for longwave infrared (LWIR) measurements from a passive instrument. This shows that this approach should permit more accurate estimation as well as a more reasonable description of estimate uncertainty.

Authors:
; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
910255
Report Number(s):
PNNL-SA-53877
NN2001000; TRN: US200723%%386
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Sensors, 7(6):905-920
Additional Journal Information:
Journal Volume: 7; Journal Issue: 6
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; ALGORITHMS; NONLINEAR PROBLEMS; GAS SPILLS; DETECTION; PLUMES; REMOTE SENSING; INFRARED THERMOGRAPHY; DATA ANALYSIS

Citation Formats

Heasler, Patrick G, Posse, Christian, Hylden, Jeff L, and Anderson, Kevin K. Nonlinear Bayesian Algorithms for Gas Plume Detection and Estimation from Hyper-spectral Thermal Image Data. United States: N. p., 2007. Web. doi:10.3390/s7060905.
Heasler, Patrick G, Posse, Christian, Hylden, Jeff L, & Anderson, Kevin K. Nonlinear Bayesian Algorithms for Gas Plume Detection and Estimation from Hyper-spectral Thermal Image Data. United States. https://doi.org/10.3390/s7060905
Heasler, Patrick G, Posse, Christian, Hylden, Jeff L, and Anderson, Kevin K. 2007. "Nonlinear Bayesian Algorithms for Gas Plume Detection and Estimation from Hyper-spectral Thermal Image Data". United States. https://doi.org/10.3390/s7060905. https://www.osti.gov/servlets/purl/910255.
@article{osti_910255,
title = {Nonlinear Bayesian Algorithms for Gas Plume Detection and Estimation from Hyper-spectral Thermal Image Data},
author = {Heasler, Patrick G and Posse, Christian and Hylden, Jeff L and Anderson, Kevin K},
abstractNote = {This paper presents a nonlinear Bayesian regression algorithm for the purpose of 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 remotesensing spectra, and the terrestrial (or atmospheric) parameters that we desire to estimate, is typically littered with many unknown "nuisance" parameters (parameters that we are not interested in estimating, but also appear in the model). 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 regression methodology is illustrated on realistic simulated data from a three-layer model for longwave infrared (LWIR) measurements from a passive instrument. This shows that this approach should permit more accurate estimation as well as a more reasonable description of estimate uncertainty.},
doi = {10.3390/s7060905},
url = {https://www.osti.gov/biblio/910255}, journal = {Sensors, 7(6):905-920},
number = 6,
volume = 7,
place = {United States},
year = {Wed Jun 13 00:00:00 EDT 2007},
month = {Wed Jun 13 00:00:00 EDT 2007}
}