Abstract-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.Index Terms-Genetic algorithm (GA), perceptual grouping, road detection, synthetic aperture radar (SAR).

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Abstract-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.Index Terms-Genetic algorithm (GA), perceptual grouping, road detection, synthetic aperture radar (SAR).

This paper presents an automatic map-based road detection algorithm for spaceborne synthetic aperture radar (SAR) images. Our goal is to find roads in a SAR image with subpixel accuracy with the help of a digital map. There are location errors between the digital map and the geocoded SAR image, which are about 20 to 30 pixels, and we adopt a coarse-to-fine strategy to reduce it. In the coarse matching step, we roughly find the locations of roads by a simple search using water areas or a generalized Hough transform based on digital map information. The fine matching step detects roads accurately by using the active contour model (snake). The input of the snake operation is the potential field constructed from the extracted ridges or ravines of curvilinear structures in the SAR image. Experimental results show that our algorithm detects roads with average error of less than one pixel. © 2000 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(00)01309-X] Subject terms: road detection; spaceborne synthetic aperture radar (SAR) image; digital map; generalized Hough transform (gHT); curvilinear structure; potential field; active contour model (snake).

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