A stochastic mixing model approach to sub-pixel target detection in hyper-spectral images

In this paper a new sub-pixel target detector for hyper-spectral images, based on the stochastic mixing model (SMM), is presented. The SMM models a mixed pixel, under the target present hypothesis, as linear combination of target and background spectra. Unlike the linear mixing model (LMM), target and background are modeled as random vectors in order to characterize their spectral variability. By assuming the target spectrum deterministic and known, a SMM based detection strategy is derived by computing the least square mean error (LSME) estimate of the target fraction in the observed pixel. This approach provides a closed form detector statistic as opposite to other SMM based detectors proposed in the literature. The new algorithm and the adaptive matched subspace detector (AMSD), based on the LMM, are applied to a MIVIS data set and the experimental results are compared by means of a suitable performance index.

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