Improving Physical and Statistical Models for Detecting Difficult Targets with LRT Detectors in Closed-Form

This work examines classical, more recent, and new hyperspectral detection algorithms that stem from the common framework of the decision-theory based statistical likelihood ratio test (LRT). Within this context, the tradeoffs involve improving models of target spectral variability, accurately characterizing the background, and producing a detector with closed-form solution. There is no algorithm that has shown universally best performance, but each of the algorithms can be specifically suited to deal with a given target detection scenario. Experimental results featuring real hyperspectral data are shown to compare the detection performance of the examined algorithms on two case-study target detection scenarios.