Global, Local, and Stochastic Background Modeling for Target Detection in Mixed Pixels

As hyperspectral sensors and exploitation methods have evolved, the accuracy of conventional background models has become a limiting factor for high confidence and low false alarm detection of mixed pixel targets. Many common target detection algorithms, such as the Adaptive Coherence/Cosine Estimator, implicitly use a global background model that assumes the background can be modeled by a single, multivariate Gaussian random variable with additive independent and identically distributed Gaussian noise. In order to improve the accuracy of the Gaussianity assumptions, a local background model, which models the background as multiple, disjoint Gaussian clusters, has also been widely considered. This paper introduces an improved variant of the local background model, as well as a novel stochastic background model that is free from distributional assumptions and accounts for the spectral variability of the background on a pixel-by-pixel basis. The performance of the global, local, and stochastic background models is evaluated on a controlled data set and the tradeoffs associated with each method are discussed.