LIU Chang-jun 1, HUANG Qian 2, Tang Yu 3, LU Yu-ping 3

1 .China Institute of Water Resources and Hydropower Research, Beijing 100038, China;
2. Water Resources Research Institute of Shandong Province, Jinan 250013, China;
3. Hydrochina Chengdu Engineering Corporation, Chengdu 610072, China

Abstract: Aiming at the problem that quick classification of buildings and vegetation is difficult to achieve for Lidar point clouds, the application of fuzzy clustering method FCM (fuzzy c-means) to the classification of buildings and vegetation for the discrete airborne laser point clouds is proposed. First of all, triangulation reconstruction by Delaunay triangulation based on planar projection is carried out according to the characteristics of the point clouds. Then, according to the different properties of the normal vector, fussy clustering is conducted by FCM and the improved orientation matrix method. Furthermore, the point clouds classification for different properties such as buildings and vegetation is achieved. This method can quickly classify point clouds and the classification results can be visualized in different colors in space. On this basis, software for three-dimensional visualization of the laser point clouds classification is developed with IDL language and named LIDARVIEW. With this software, airborne point clouds in one region were selected for the data classification experiments. The experimental results show that: (1) Delaunay Triangulation based on planar projection is particularly suitable for the rapid TIN (Triangulated irregular network) construction of airborne LIDAR point clouds, having the advantage of faster speed and high efficiency; (2) Application of the fuzzy clustering method (FCM) and the improved orientation matrix method is suitable for vegetation and buildings classification for airborne Lidar point clouds, being fast and effective; (3) Results for airborne Lidar point clouds by FCM are reliable and reasonable, with a strong versatility and generalization.

Key words: Airborne LIDAR data; Point Cloud Classification; FCM; fuzzy clustering

Published in: Journal of China Institute of Water Resources and Hydropower Research (Vol. 11 No. 3, 2013)