Building detection from urban SAR image using building characteristics and contextual information

With the urgent demand on urban synthetic aperture radar (SAR) image interpretation, this article deals with detecting buildings from a single high-resolution SAR image. Based on our previous work in building detection from SAR images, aiming at extracting buildings with their whole and accurate boundaries from the built-up area, a general framework using the marker-controlled watershed transform is introduced to combine both building characteristics and contextual information. First, the characteristics of the buildings and their surroundings are extracted as markers by the target detection techniques. Second, the edge strength image of the SAR image is computed using the ratio of exponentially weighted averages detector. The marker-controlled watershed transform is implemented with the markers and the edge strength image to segment buildings from the background. Finally, to remove false alarms, building features are considered. Especially, a shape analysis method, called direction correlation analysis, is designed to keep linear or L-shaped objects. We apply the proposed method to high-resolution SAR images of different scenes and the results validate that the new method is effective with high detection rate, low false-alarm rate, and good localization performance. Furthermore, comparison between the new method and our previous method reveals that introducing contextual information plays an important role in improve building detection performance.