基于FCM 的机载LIDAR 数据的建筑物和植被分类方法

Classification method for construction and vegetable of airborne LIDAR data based on FCM clustering arithmetic

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 character- istics 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 classifica- tion 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, soft- ware for three-dimensional visualization of the laser point clouds classification is developed with IDL lan- guage 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） Applica- tion of the fuzzy clustering method （FCM） and the improved orientation matrix method is suitable for vegeta- tion 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.