机载LiDAR点云数据建筑物提取方法适应性研究  

Adaptability Study on the Building Extraction Methods Based on Airborne LiDAR Point Cloud Data by

作　　者：李开伟[1,2] 刘锟铭 郭加伟 LI Kaiwei

机构地区：[1]四川水利职业技术学院测绘工程系,四川成都611231 [2]国土资源部地学空间信息技术重点实验室,四川成都610059 [3]西南交通大学地球科学与环境工程学院,四川成都610031

出　　处：《地理空间信息》2018年第7期19-22,123,共5页Geospatial Information

基　　金：国土资源部地学空间信息技术重点实验室开放基金资助项目(KLGSIT2015-03)

摘　　要：为解决机载LiDAR点云数据建筑物提取精度不高的问题,首先分析了现有的基于机载LiDAR点云数据的建筑物提取方法;然后综合地形、树木、建筑物密度等对建筑物提取的影响,以德国斯图加特市法伊英根的LiDAR点云数据为例进行了建筑物提取实验;最后对提取结果进行了定量精度评定。结果表明,基于影像的机载LiDAR点云数据建筑物提取精度为93.1%;而基于数学形态学图像的处理方法和基于Delaunay三角剖分的方法受建筑物形状和地形等限制较多,提取精度分别为87.6%和81.3%,说明基于影像的机载LiDAR点云数据建筑物提取方法的准确性较高,限制性条件较少。To improve the accuracy of building extraction based on airborne LiDAR point cloud data,we analyzed several existing building extraction methods in this paper at first.And then,we selected Fey Ingen City building LiDAR point cloud data to experiment the extraction of buildings in consideration of the terrain,trees,buildings and buildings density.Finally,we made quantitative precision assessment of the extraction results.The results show that the building extraction accuracy based on airborne LiDAR point cloud data is 93.1,while the image processing method based on mathematical morphology and Delaunay triangulation methods are much more restricted by the building shape,terrain and others and their accuracy are 87.6 and 81.3 respectively.The study shows that the building extraction accuracy based on airborne LiDAR point cloud data is much higher and less restricted.

关 键 词：LIDAR点云 建筑物提取 DSM 形态学 DELAUNAY 

分 类 号：P231.1[天文地球—摄影测量与遥感]