Classification Algorithm of Urban Point Cloud Data based on LightGBM

Abstract

In order to improve the accuracy and efficiency of airborne LiDAR point cloud data classification algorithm, a classification algorithm of point cloud based on LightGBM was proposed, and the classification effect of the algorithm on urban point cloud data was tested. In this paper, LightGBM-1 classifier was used to roughly classify point cloud data firstly. Then ground points were extracted to normalize non-ground points. After that, multi-scale neighborhood features of building points and vegetation points were extracted, and then building points and vegetation points were finely classified by LightGBM-2 classifier. The algorithm was verified by urban point cloud data, and the classification effect was evaluated by analyzing classification accuracy and time. Experimental results show that, compared with other algorithms, this algorithm can effectively improve the effect of point cloud data, and realize the effective classification of point cloud data in urban areas.