Remote sensing
Remote sensing
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DOI：
10.1177/030913339902300207
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发表时间：
1999-06
期刊：
影响因子：
--
通讯作者：
Daniel N.M. Donoghue
中科院分区：
文献类型：
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作者：
Daniel N.M. Donoghue
点云滤波是数字地形模型（DTM）生产中的重要环节。尽管到目前为止，在这一领域已经进行了大量的研究，但仍然有一些问题尚未解决，特别是在复杂地形中。事实上，在所提出的点云滤波方法中使用用户定义的参数，以及与地形坡度和地上物体多样性的增加并行的参数估计的难度，降低了滤波成功率。另一个问题是要研究的点云密度的适当规范。点云密度通常是根据DTM的地面采样距离来确定的，它影响点云滤波过程的成功，从而影响生成的DTM的精度。在这项研究中，五个不同密度的无人机（UAS）为基础的点云过滤使用两种不同的点云过滤算法布模拟过滤（CSF）和gLiDAR检查点云密度对过滤成功的影响。研究发现，点云滤波性能随点密度的增加而降低。
Point cloud filtering is an important step in Digital Terrain Model (DTM) production. Despite the fact that a great body of research has been conducted in this area so far, there are still some problems that have not yet been solved, especially in complex terrains. The fact that the use of user-defined parameters within the presented point cloud filtering methods, and the difficulty of parameter estimation in parallel to the increase in the topography slope and above-ground object diversity, decreases the filtering success. Another problem is the proper specification of the point cloud density to be studied. Point cloud density, which is generally specified considering the ground sampling distance of the DTM, influences the success of the point cloud filtering process, therefore, the accuracy of the DTM produced. In this study, five Unmanned Aerial System (UAS)-based point clouds of different densities were filtered using two different point cloud filtering algorithms Cloth Simulation Filtering (CSF) and gLiDAR to examine the impacts of the point cloud density on filtering success. It was found that the point cloud filtering performance decreased as the point density increased.