ABSTRACT

Most ground filtering algorithms are primarily designed for airborne LiDAR point cloud processing and their successful use in identifying ground points from photogrammetric point clouds remains questionable. We compared six ground filtering algorithms implemented in Metashape, ArcGIS, CloudCompare, LAStools, and PDAL. We used UAV photogrammetry-based (acquired under leaf-off conditions) and airborne LiDAR (leaf-on) point clouds of the same area to: (i) compare accuracy of generated DTMs; (ii) evaluate the effect of vegetation density and terrain slope on filtering accuracy; and (iii) assess which algorithm parameters have the greatest effect on the filtering accuracy. Our results show that the performance of filtering algorithms was affected by the point cloud type, terrain slope and vegetation cover. The results were generally better for LiDAR (RMSE 0.13–0.19 m) than for photogrammetric (RMSE 0.19–0.23 m) point clouds. The behavior in varying vegetation and terrain conditions was consistent for LiDAR point clouds. However, when applied on photogrammetric point clouds, the algorithms’ behavior was inconsistent, especially in areas of steep slope (except for the Progressive Triangulated Irregular Network in LAStools). Parameters related to the selection of the initial minimum elevation ground points were the most influential in all algorithms and point clouds.

大多数地面滤波算法主要是为机载LiDAR点云处理而设计的，它们在从摄影测量点云中识别地面点的成功使用仍然值得怀疑。我们比较了在Metashape，ArcGIS，CloudCompare，LAStools和PDAL中实现的六种地面滤波算法。我们使用基于无人机摄影测量（在离开条件下获取）和相同区域的机载LiDAR（叶上）点云来：（i）比较生成的DTM的准确性；（ii）评估植被密度和坡度对过滤精度的影响；（iii）评估哪些算法参数对滤波精度影响最大。我们的结果表明，滤波算法的性能受点云类型，地形坡度和植被覆盖率的影响。对于LiDAR（RMSE 0.13–0.19 m），结果通常好于摄影测量（RMSE 0.19–0.23 m）点云。LiDAR点云在变化的植被和地形条件下的行为是一致的。但是，将其应用于摄影测量点云时，算法的行为却不一致，尤其是在陡坡区域（LAStools中的渐进三角不规则网络除外）。与初始最小高程地面点的选择有关的参数在所有算法和点云中影响最大。尤其是在陡坡地区（LAStools中的渐进三角不规则网络除外）。与初始最小高程地面点的选择相关的参数在所有算法和点云中影响最大。特别是在陡坡地区（LAStools中的渐进三角不规则网络除外）。与初始最小高程地面点的选择相关的参数在所有算法和点云中影响最大。