K-Plane-Based Classification of Airborne LiDAR Data for Accurate Building Roof Measurement

Abstract

A new classification method based on the k-plane clustering algorithm is proposed to segment the point cloud of a building roof, which is obtained from an airborne light detection and ranging (LiDAR) instrument. In the operation of laser points clustering, 3-D coordinates of laser points in the point cloud are directly used as clustering objects. Fitting planes of laser points in the clusters are generated from the obtained clustering solution, and intersecting lines of the fitting planes are calculated. Using the intersecting lines, the point cloud of the building roof is then segmented. Since calculation for the clustering objects, i.e., the normal vectors of neighboring planes of the laser points, required in the classification methods based on the fuzzy k-means clustering algorithm is avoided in the proposed method, not only is the complexity of the classification procedure reduced, but also the accuracy of classification result is improved. In addition, in the proposed method, to guarantee the effectiveness of the k-plane algorithm, the initial cluster planes are estimated from the elevation image of building roof in advance before the process of clustering operation. The proposed k-plane-based classification method is validated by using a number of real airborne LiDAR point clouds.