Characterizing Clutter in the Context of Detecting Weak Gaseous Plumes in Hyperspectral Imagery

Weak gaseous plume detection in hyperspectral imagery requires that background clutter consisting of a mixture of components such as water, grass, and asphalt be well characterized. The appropriate characterization depends on analysis goals. Although we almost never see clutter as a single-component multivariate Gaussian (SCMG), alternatives such as various mixture distributions that have been proposed might not be necessary for modeling clutter in the context of plume detection when the chemical targets that could be present are known at least approximately. Our goal is to show to what extent the generalized least squares (GLS) approach applied to real data to look for evidence of known chemical targets leads to chemical concentration estimates and to chemical probability estimates (arising from repeated application of the GLS approach) that are similar to corresponding estimates arising from simulated SCMG data. In some cases, approximations to decision thresholds or confidence estimates based on assuming the clutter has a SCMG distribution will not be sufficiently accurate. Therefore, we also describe a strategy that uses a scene-specific reference distribution to estimate decision thresholds for plume detection and associated confidence measures.