Detection and Characterization of Chemical VaporFugitive Emissions from Hyperspectral Infrared Imagery byNonlinear Optimal Estimation

摘要

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