In this paper, a road network grouping algorithm for Synthetic Aperture Radar (SAR) images is proposed by exploiting multiscal e geometric analysis of detector responses. Before running the algorithm, a response map made up of responses, which is binarized, skeletonized, and vectorized to generate road candidates, is obtained by applying a local detector to a SAR image first. Then the proposed method identifies real road segments among the candidates and fills gaps between them. It works in three steps. 1) Guidance segments are extracted at different resolutions from the response map using multiscale techniques and merged to get a more appropriate approximation. 2) Segments are labeled “road” or “noise” using relaxation labeling techniques, among which “road” ones are grouped as they may lie on different roads. 3) Connection points between can didates are acquired by mapping candidates to grouped “road” guidance segments. Those connection points are linked with straight lines or curvilinear segments after a segmentation process. The experiments on TerraSAR images show the effectiveness of this new method.

Peer Reviewed