  The generalized likelihood ratio test (GLRT) is used to derive a detector for
solid sub-pixel targets in hyperspectral imagery. A closed-form solution is
obtained that optimizes the replacement target model when the background is a
fat-tailed elliptically-contoured multivariate t-distribution. This generalizes
GLRT-based detectors that have previously been derived for the replacement
target model with Gaussian background, and for the additive target model with
an elliptically-contoured background. Experiments with simulated hyperspectral
data illustrate the performance of this detector in various parameter regimes.
