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DecHPoints: A New Tool for Improving LiDAR Data Filtering in Urban Areas
PFG-Journal of Photogrammetry, Remote Sensing and Geoinformation Science ( IF 4.1 ) Pub Date : 2020-02-04 , DOI: 10.1007/s41064-019-00088-7
Sandra Buján , Chester Andrew Sellers , Miguel Cordero , David Miranda

Identifying ground points from LiDAR data remains a challenge more than 2 decades after automatic filtering methods were first developed. The efficacy of filtering methods depends on both the physical characteristics of the environment and on the quality of the data used. Other limitations, affecting accessibility and usability, include the choice of filter and identification of optimal parameter values. To address these problems, the most recent filters have increased their level of complexity combining different strategies, so-called hybrid methods. In this study, two tools are proposed to improve the previous filters: a decimation tool for non-ground points and a densification process. Our main improvement is to combine these tools and a filter, in this case the Iterative Robust Interpolation Filter (IRI) (Kraus and Pfeifer in ISPRS J Photogramm Remote Sens 53(4):193–203. https://doi.org/10.1016/S0924-2716(98)00009-4, http://www.sciencedirect.com/science/article/pii/S0924271698000094, 1998), to (1) improve the filtering results in urban areas by removing buildings prior to filtering, which enables a downsizing of cells used for the selection of ground points and (2) to reduce the influence of parameters on the filtering accuracy. We used two LiDAR data sets: the reference data were acquired from the International Society of Photogrammetry and Remote Sensing (ISPRS) and the high density LiDAR data. In the first case, the results obtained are compared with those obtained in previous studies, using the metrics proposed by Sithole and Vosselman (ISPRS J Photogramm Remote Sens 59(1–2):85–101, https://doi.org/10.1016/j.isprsjprs.2004.05.004, http://www.sciencedirect.com/science/article/pii/S0924271604000140, 2004). For urban samples, the proposed hybrid method provided better results than the IRI algorithm, yielding a Kappa coefficient of 91.5%. The proposed method is one of the most accurate filters that has been tested with the ISPRS data. Finally, the results obtained on the basis of the high density LiDAR data reinforced the previous results and showed the potential usefulness of the proposed hybrid method.



中文翻译:

DecHPoints:改善市区LiDAR数据过滤的新工具

自首次开发自动滤波方法以来,从LiDAR数据中识别地面点仍然是一个挑战。过滤方法的有效性取决于环境的物理特征和所用数据的质量。影响访问性和可用性的其他限制包括过滤器的选择和最佳参数值的标识。为了解决这些问题,最新的过滤器结合了不同的策略,即所谓的混合方法,提高了其复杂度。在这项研究中,提出了两种工具来改进以前的过滤器:用于非接地点的抽取工具和致密化过程。我们的主要改进是将这些工具和过滤器结合在一起,在这种情况下,迭代鲁棒插值滤波器(IRI)(ISPRS J Photogramm Remote Sens 53(4):193-203中的Kraus和Pfeifer。https://doi.org/10.1016/S0924-2716(98)00009-4, http://www.sciencedirect.com/science/article/pii/S0924271698000094,1998)至(1)通过在过滤之前移走建筑物来改善市区的过滤效果,从而可以缩小用于选择过滤器的单元的尺寸接地点和(2)减少参数对滤波精度的影响。我们使用了两个LiDAR数据集:参考数据来自国际摄影测量与遥感学会(ISPRS)和高密度LiDAR数据。在第一种情况下,使用Sithole和Vosselman(ISPRS J Photogramm Remote Sens 59(1-2):85-101,https://doi.org/10.1016/j.isprsjprs.2004.05.004,http://www.sciencedirect.com/science/article/pii/S0924271604000140,2004)。对于城市样本,所提出的混合方法比IRI算法提供更好的结果,得出的Kappa系数为91.5%。所提出的方法是经过ISPRS数据测试的最准确的过滤器之一。最后,基于高密度LiDAR数据获得的结果加强了先前的结果,并表明了所提出的混合方法的潜在实用性。所提出的方法是经过ISPRS数据测试的最准确的过滤器之一。最后,基于高密度LiDAR数据获得的结果加强了先前的结果,并表明了所提出的混合方法的潜在实用性。所提出的方法是经过ISPRS数据测试的最准确的过滤器之一。最后,基于高密度LiDAR数据获得的结果加强了先前的结果,并表明了所提出的混合方法的潜在实用性。

更新日期:2020-02-04
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