Object-based Point Clouds Classification of the Vegetation and Building Overlapped Area
XU Hong-gen1,2, WANG Jian-chao1, ZHENG Xiong-wei1, WU Fang1, LI Qian1
1. China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China;
2. Wuhan Center of Geological Survey, Wuhan 430205, China
Abstract：This paper proposes an object-oriented point clouds classification method for solving the difficult classification problem for the overlapping between vegetation and buildings based on reviewing current status of LiDAR point clouds classification approaches. In the proposed method, the point clouds are firstly separated into ground points and non-ground points through adaptive TIN filter method, and the DTM is obtained. Second, a triangle network is constructed for non-ground points higher than DTM. The non-ground point clouds could be divided into multi-objects by removing longer edges (edge between ground and object). Then, the object is judged to decide whether it belongs to vegetation or building according to its information entropy of triangle network slope. Finally, for objects difficult to be distinguished from other objects, the overlapped area between vegetation and buildings is extended by geometric shape of buildings, so that the accuracy of point clouds classification of the overlapped area could be improved. The experiment results show good classification performance for buildings and vegetation, and the accuracy reaches 87%.
徐宏根, 王建超, 郑雄伟, 吴芳, 李迁. 面向对象的植被与建筑物重叠区域的点云分类方法[J]. 国土资源遥感, 2012, 24(2): 23-27.
XU Hong-gen, WANG Jian-chao, ZHENG Xiong-wei, WU Fang, LI Qian. Object-based Point Clouds Classification of the Vegetation and Building Overlapped Area. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(2): 23-27.
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2012, Vol. 24 
