OPTIMUM TEXTURE ESTIMATORS FOR SAR CLUTTER

This paper analyses various estimators for characterizing synthetic aperture radar clutter textures. First, we consider maximum likelihood estimators, which require specific knowledge of the form of the probability distribution of the data but would be expected to yield the best performance. Both K- and Weibull-distributed clutter models, which are often applied to characterize natural SAR clutter, are considered. Though a full maximum likelihood solution is impossible for the K distribution, we derive an approximate one for the multi-look case. We next derive expressions for limiting errors in a variety of direct texture estimators and compare their predicted performance with the maximum likelihood estimates in a search for robust, optimum texture estimators.