Non Zero Mean Adaptive Cosine Estimator and Application to Hyperspectral Imaging

Abstract

We develop Adaptive Cosine Estimator (ACE) type detector for non-zero mean Gaussian interference specifically for the replacement and additive target models of the hyperspectral imaging problem. We consider the case where the data under test and the training samples differ from one scaling factor on the mean and one scaling factor on the covariance matrix. We derive two-step generalized likelihood ratio tests for both the additive model and the replacement model and show that the new detectors differ in the way the mean value is removed. A real data experiment shows that they outperform the standard version.