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Integration of different filter algorithms for improving the ground surface extraction from airborne LiDAR data

机译：集成了不同的滤波算法，以改善从机载LiDAR数据中提取地面的能力

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摘要

An important step for processing airborne Light Detection And Ranging (LiDAR) data is point cloud filtering. Points striking on vegetation and man-made objects and low points (points significantly lower than neighboring points) are filtered out, leaving ground points for generation of digital terrain models (DTM). A variety of filter algorithms have been developed, which have disparate performance in different landscape and environment. This study investigates the potential of integrating the results of different filter algorithms for improving the ground surface extraction from the LiDAR point cloud. A simple procedure was proposed based on a statistical approach to identify and remove filtering errors and combine ground points from each filtering result. The procedure was tested in an area with rugged terrain covered by dense vegetation of variable heights. The filtering results of two popular filter algorithms, progressive TIN (Triangulated Irregular Network) densification and hierarchical robust interpolation, were integrated. The filtering results of two algorithms and the integration result were qualitatively evaluated. The evaluation results indicated that the proposed integration procedure can remove most vegetation points that were not filtered out by filter algorithms, and combine ground points from each filtering result.
机译：处理机载光检测和测距（LiDAR）数据的重要步骤是点云过滤。撞击在植被和人造物体上的点和低点（远低于相邻点的点）被滤除，留下地面点以生成数字地形模型（DTM）。已经开发了多种滤波器算法，它们在不同的景观和环境下具有不同的性能。这项研究调查了整合不同滤波器算法结果以改善从LiDAR点云提取地面的潜力。提出了一种基于统计方法的简单程序，以识别和消除滤波错误并结合每个滤波结果中的接地点。该程序在崎by不平的地形上被高低不一的茂密植被覆盖。集成了两种流行的滤波算法，渐进式TIN（不规则三角网）致密化和分层鲁棒插值的滤波结果。定性地评价了两种算法的滤波结果和积分结果。评价结果表明，所提出的整合程序可以去除大多数未被滤波算法滤除的植被点，并结合每个滤波结果中的地面点。

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    Deng, SS; Shi, WZ;

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  • 年度 2013
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  • 正文语种 en
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