New detector based on patch segmentation for high resolution SAR image

This paper proposes a new detector for interested object detection in high resolution SAR image. To obtain higher performance, four parameters are required to describe features of the interested object. Specifically, the parameters of segmentation scale and average expected object size are used for SAR image segmentation to get patches, which are small image parts to capture fine scale information such as gray variance and shape. In this step, adjacent map is adopted to recode the information and the relationship of all the patches; The third parameter is false alarm rate, which is used to retrieve the intensity threshold for finding out all the potential targets in SAR image; The parameter of object packing density is finally used to reduce the false alarm, which is calculated according to their occurrence frequency. To evaluate the proposed detection methodology, air-born SAR image with one-meter resolution is tested. The scene context of this image is complex, since it includes building, road, vegetation and trucks. If the trucks are taken as the interested objects, this methodology can detect all of them with lower false alarm rate.