针对LiDAR点云建筑物提取的研究很多,而在农村区域植被与建筑粘连且高度近似情况下的讨论较少。考虑到中国典型农村区域建筑特征,以湖南省益阳市泗湖山镇为研究区进行LiDAR点云的建筑提取研究。首先利用梯度及梯度方向约束的形态学滤波方法,将原始点云滤波得到地面点,对地面点及原始点云进行插值得到数字高程模型(DEM)和数字地表模型(DSM);然后通过两者相减得到归一化的NDSM,进而对NDSM进行高程及梯度双约束的标记分水岭变换得到地物对象;最后建立特征指标,对地物对象进行最大似然分类得到建筑物对象。研究表明建筑物分类的生产者精度和用户精度均>90%, Kappa系数>0.8, 构建方法在农村地区建筑提取研究中取得了良好效果。
Abstract
Many studies have focused on the extraction of buildings from Light Detection and Ranging (LiDAR) point cloud information, but few have investigated the process in rural areas where vegetation and buildings are of similar heights and interconnected. With Sihushan County in Hunan Province which has typical rural building characteristics as study area, the building information is extracted using LiDAR point cloud data. A modified morphological filter in which gradient and gradient direction of primitive point were used to constraint the area of filtering is adopted. The interpolated ground points and primitive points were used to obtain a digital elevation model (DEM) and a digital surface model (DSM), and the two were subtracted to derive a normalized DSM (NDSM). Then, a transformation of the sign watershed was conducted under control of both height and gradient to obtain ground objects. Finally, using built feature indicators, building objects were identified based on a maximum likelihood classification. Results show that the user accuracy and producer accuracy of the building extraction are both greater than 90%, and the Kappa coefficient is greater than 0.8, which suggest that the proposed method achieved good results in building extraction in rural areas.
关键词
LiDAR /
形态学滤波 /
分水岭算法 /
特征值 /
最大似然分类
Key words
LiDAR /
morphological filter /
watershed algorithm /
features /
maximum likelihood classification
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基金
国家重点研发计划项目(2018YFC0407804);国家自然科学基金项目(41501558)
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