Enhanced detection of chemical plumes in hyperspectral images and movies throughimproved backgroundmodeling

We extend recent work that models the background in hyperspectral images by a single distribution (Gaussian or subspace) to use a mixture of such distributions. This seems to better capture the complexity of the background, which often consists of heterogeneous regions (e.g., sky, mountain and ground). We derive mixture versions of the previous estimators and apply them to benchmark data sets for detecting chemical plumes of known chemicals in hyperspectral images and movies. Our experiments show that the mixture background models consistently outperform their counterparts with a single distribution.