Road detection in spaceborne SAR images using a genetic algorithm

This paper presents a technique for the detection of roads in a spaceborne synthetic aperture radar (SAR) image using a genetic algorithm (GA). Roads in a spaceborne SAR image can be modeled as curvilinear structures that possess width. Curve segments, which represent the candidate positions for roads, are extracted from the image using a curvilinear structure detector, and the roads are accurately detected by grouping those curve segments. For this purpose, we designed a grouping method based on a GA, which is a global optimization method. We combined perceptual grouping factors with it and tried to reduce its overall computational cost by introducing a concept of region growing. In this process, a selected initial seed is grown into a finally grouped segment by the iterated GA process, which considers segments only in a search region. To detect roads more accurately, postprocessing, including noisy curve segment removal, is performed after grouping. We applied our method to ERS-1 SAR and SIR-C/X-SAR images that have a resolution of about 30 m. The experimental results show that our method can accurately detect road networks as well as single-track roads and is much faster than a globally applied GA approach.