New statistical detector for known spectral signature targets in hyper-spectral images

This paper deals with the sub-pixel target detection problem in hyper-spectral images. The problem is approached by modeling the mixed spectrum with both the Linear Mixing Model (LMM) and the Stochastic Mixing Model (SMM). A detection strategy is derived by assuming the SMM. In the proposed algorithm, detection is accomplished by testing the values of the Maximum A-priori Probability (MAP) estimate of the target’s abundance that represent the fraction of the spectrum in the observed pixel due to the target. The algorithm has been applied to experimental images and the results have been compared with the ones obtained by the Adaptive Matched Subspace Detector (AMSD) based on the LMM.