Knowledge-based segmentation of SAR data with learned priors

An approach for the segmentation of still and video synthetic aperture rada r (SAR) images is described in this note, A priori knowledge about the obje cts present in the image, e.g,, target, shadow, and background terrain, is introduced via Bayes' rule, Posterior probabilities obtained in this may ar e then anisotropically smoothed, and the image segmentation is obtained via MAP classifications of the smoothed data. When segmenting sequences of ima ges, the smoothed posterior probabilities of past frames are used to learn the prior distributions in the succeeding frame. We show with examples from public data sets that this method provides an efficient and fast technique for addressing the segmentation of SAR data.