Detection and characterization of chemical vapor fugitive emissions from hyperspectral infrared imagery by nonlinear optimal estimation

Christopher M. Gittins

doi:10.1117/12.850140

12 May 2010 Detection and characterization of chemical vapor fugitive emissions from hyperspectral infrared imagery by nonlinear optimal estimation

Christopher M. Gittins

Author Affiliations +

Proceedings Volume 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI; 76951S (2010) https://doi.org/10.1117/12.850140
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

The clutter-matched filter (CMF) and the Adaptive Cosine Estimator (ACE) have become established metrics for detecting chemical vapor plumes from hyperspectral infrared imagery. Both metrics follow from the presumption of a linear additive signal model. However, examination of the underlying radiative transfer equation (RTE) indicates that while the use of a linear additive signal model is a reasonable approximation when considering an optically-thin plume viewed against blackbody background the RTE is in fact nonlinear. Unfortunately, presumption of a linear additive signal model can significantly degrade plume detection statistics and results in significant errors in estimated chemical vapor column density when plumes are not optically-thin or are viewed against spectrally-complex backgrounds. This paper describes a nonlinear estimation approach which integrates a parameterized signal model based on the RTE with a statistical model for the infrared background. We show results obtained by applying the nonlinear estimation approach to background-only hyperspectral imagery augmented with synthetic chemical vapor plumes and compare them with results obtained presuming a linear additive signal model. As plumes become optically-thick, nonlinear estimation yields significantly more accurate estimates of chemical vapor column density and significantly more favorable plume detection statistics than clutter-matched-filter-based and adaptive-subspace-detector-based plume characterization and detection.

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Christopher M. Gittins "Detection and characterization of chemical vapor fugitive emissions from hyperspectral infrared imagery by nonlinear optimal estimation", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76951S (12 May 2010); https://doi.org/10.1117/12.850140

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KEYWORDS

Chemical analysis

Statistical analysis

Nonlinear optics

Detection and tracking algorithms

Sensors

Thermal effects

Absorbance

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