Building detection in high resolution satellite urban image using segmentation, corner detection combined with adaptive windowed Hough Transform

The building detection is one of the most challenging issues in remote sensing image processing. In this paper, a novel approach for building detection using corner detection, segmentation and adaptive windowed Hough Transform is presented. In the first, the Mean shift segmentation is used to split the image into a numbers of classes. In the second step, the scale invariant feature transform (SIFT) is used to extract the corners in the original image. In the third step the corners are used as one of the evidences to verify the presence of buildings. In the Mean shift segmentation result image, around the corners detected by SIFT algorithm, the approximate boundary of the buildings is extracted. With the help of approximate boundary of the buildings, the size of the building can be estimate. Finally, in order to extract the precise building roof boundary, the adaptive windowed Hough Transform is used to extract the straight line of the building boundary. Preliminary experimental results indicate that the proposed method produced promising results.