Comparison of Three Unsupervised Segmentation Algorithms for SAR Data in Urban Areas

This paper presents a preliminary comparison of mean shift segmentation, efficient graph-based segmentation and normalized cuts for segmenting meter-resolution SAR data in urban areas. The small patches generated by these bottom-up segmentation algorithms provide spatial support for object detection. We evaluate their performances on ground truth data by varying their parameters. In order to obtain better spatial support, we apply multiple segmentations to a single segmentation of each algorithm. The multiple segmentations are representative samplings of the entire segmentation space. The experimental results demonstrate that the three algorithms are promising for SAR image segmentation, and that the multiple segmentations improves the abilities of providing spatial support.