Closed-form detector for solid sub-pixel targets in multivariate t-distributed background clutter
This work addresses target detection in hyperspectral imagery for remote sensing applications, but it is incremental as it extends existing models to a more general background distribution.
The paper tackled detecting solid sub-pixel targets in hyperspectral imagery by deriving a closed-form generalized likelihood ratio test detector for a multivariate t-distributed background, generalizing prior Gaussian and elliptically-contoured models, with experiments on simulated data showing performance across parameter regimes.
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.