Detection and Characterization of Chemical Vapor Fugitive Emissions from Hyperspectral Infrared Imagery by Nonlinear Optimal Estimation

摘要

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.