摘要
为了从海量的LiDAR点云中分离出建筑物点类,先利用渐进式不规则三角网加密法过滤出地面点类,然后根据建筑物顶面的主成分和法向量分类出建筑物顶面点;在区域增长法分割出初始建筑物面片后,通过面片的形状属性删除围墙面域,并以数学形态学的闭算子填充小的孔洞;最后以建筑物面积阈值剔除小的植被区域。以3个典型的复杂城市区域为例测试算法的提取效果,结果显示建筑物的探测率和探测质量优于92%。
The airborne LiDAR technology provides a new and rapid way for detecting 3 D building. In order to separate building points from mass LiDAR point clouds, a progressive densifying TIN method is first used to filter terrain points, and then top surface points of building are classified with principal component and normal vector. After initial building facets are segmented with region growing method, some fence regions are removed and small holes are filled by using morphological closing operator. Ultimately, small vegetation regions are erased on the basis of area criterion of building. Three sampled complex urban areas are selected to test extraction effects of building. The results show that the efficiency and quality of building defection with this method are better than 92%.
出处
《大地测量与地球动力学》
CSCD
北大核心
2013年第2期124-127,共4页
Journal of Geodesy and Geodynamics
基金
国家自然科学基金(41071328)
关键词
机载LIDAR
点云
建筑物
面片
提取
：airborne LiDAR
point clouds
building
facets
extraction
