Knowledge based object detection using SAR images

One of the basic research problems in analyzing synthetic aperture radar (SAR) imagery is the detection of man-made objects. This paper presents a detection paradigm composed of an adaptive segmentation algorithm based on a priori knowledge of objects followed by a top-down hierarchical detection process that generates and evaluates object hypotheses. At the end of this process the most likely hypothesis for the object is selected. The hierarchical structure allows the use of shadow information and inter-object relationships to improve performance over that of a statistical detector based only on the properties of individual objects.