EC-GLRT: Detecting Weak Plumes in Non-Gaussian Hyperspectral Clutter Using an Elliptically-Contoured Generalized Likelihood Ratio Test

We investigate the behavior of a detector for weak gaseous plumes in hyperspectral imagery that can be derived in terms of a generalized likelihood ratio test (GLRT) applied to an elliptically-contoured (EC) model for the distribution of background clutter. Two limiting cases of this EC-GLRT detector are the adaptive matched filter (AMF) and the adaptive coherence estimator (ACE). While the general EC-GLRT detector does not share the specific optimality or invariance properties exhibited by these limiting cases, it provides an in-between model that can be competitive with both of them over a broad range of scenarios.

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