OPTIMUM TEXTURE ESTIMATORS FOR SAR CLUTTER

This paper analyses various estimators for characterizing synthetic ap erture radar clutter textures. First, we consider maximum likelihood e stimators, which require specific knowledge of the form of the probabi lity 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. Th ough a full maximum likelihood solution is impossible for the K distri bution, 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 li kelihood estimates in a search for robust, optimum texture estimators.