CLUSTERING OF 3D LINE SEGMENTS USING CENTROID NEURAL NETWORK FOR BUILDING DETECTION

An efficient method for rectangular boundary extraction from aerial image data is proposed in this paper. A centroid neural network (CNN) with a metric utilizing line segments is adopted to group low-level line segments for detecting rectangular objects. The proposed method extracts rectangular boundaries for building rooftops from 3D edge images with various types of noises arising from the stereo matching process. In order to overcome the noises in 3D edge images including line segments of shadows, a clustering method utilizes the constraint where the heights of building rooftops are similar and the clustering process is performed with candidate 3D line segments with similar heights. Experiments are performed with a set of high-resolution satellite image data. The results show that the proposed method can remove noisy segments including shadow lines efficiently and thus find more accurate rectangular boundaries and building information.