Road extraction from high-resolution SAR imagery using Hough transform

This paper presents a technique for the extraction of roads in a high resolution synthetic aperture radar (SAR) image using Hough transform. Roads in a high resolution SAR image can be modeled as a homogeneous dark area bounded by two parallel boundaries. Dark areas, which represent the candidate positions for roads, are extracted from the image using a Gaussian probability iteration segmentation, and the roads are accurately detected by Hough transform. For this purpose, we designed an average Hough transform, which is more reasonable than general Hough transform for the extraction of lines. We search the peak values in Hough space and try to reduce its overall computational cost by introducing a global CFAR detector. In this process, to detect roads more accurately, post-processing, including noisy dark regions removal and false roads removal, is performed. We applied our method to MSTAR clutter images of Redstone that have a resolution of about 1 ft × 1 ft. The experimental results show that our method can accurately detect roads.