Orientation-selective building detection in aerial images

This paper introduces a novel aerial building detection method based on region orientation as a new feature, which is used in various steps throughout the presented framework. As building objects are expected to be connected with each other on a regional level, exploiting the main orientation obtained from the local gradient analysis provides further information for detection purposes. The orientation information is applied for an improved edge map design, which is integrated with classical features like shadow and color. Moreover, an orthogonality check is introduced for finding building candidates, and their final shapes defined by the Chan-Vese active contour algorithm are refined based on the orientation information, resulting in smooth and accurate building outlines. The proposed framework is evaluated on multiple data sets, including aerial and high resolution optical satellite images, and compared to six state-of-the-art methods in both object and pixel level evaluation, proving the algorithm's efficiency.