A new method for building roof segmentation from airborne LiDAR point cloud data

A new method based on the combination of two kinds of clustering algorithms for building roof segmentation from airborne LiDAR (light detection and ranging) point cloud data is proposed. The K-plane algorithm is introduced to classify the laser footprints that cannot be correctly classified by the traditional K-means algorithm. High-precision classification can be obtained by combining the two aforementioned clustering algorithms. Furthermore, to improve the performance of the new segmentation method, a new initialization method is proposed to acquire the number and coordinates of the initial cluster centers for the K-means algorithm. In the proposed initialization method, the geometrical planes of a building roof are estimated from the elevation image of the building roof by using the mathematical morphology and Hough transform techniques. By calculating the number and normal vectors of the estimated geometrical planes, the number and coordinates of the initial cluster centers for the K-means algorithm are obtained. With the aid of the proposed initialization and segmentation methods, the point cloud of the building roof can be rapidly and appropriately classified. The proposed methods are validated by using both simulated and real LiDAR data.