Automatic DTM extraction from airborne LiDAR based on expectation-maximization
Automatic DTM extraction from airborne LiDAR based on expectation-maximization
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DOI：
10.1016/j.optlastec.2018.10.051
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发表时间：
2019-04
影响因子：
5
通讯作者：
Z. Hui;Dajun Li;Shuanggen Jin;Yao Yevenyo Ziggah;Leyang Wang;Youjian Hu
中科院分区：
文献类型：
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作者：
Z. Hui;Dajun Li;Shuanggen Jin;Yao Yevenyo Ziggah;Leyang Wang;Youjian Hu
地面点云滤波是机载LiDAR点云应用的关键步骤。虽然近年来提出了许多滤波算法，但大多数算法都存在参数设置或阈值微调的问题。这通常是耗时的，并且降低了所应用算法的自动化程度。为了克服这些问题，本文提出了一种基于期望最大化（EM）的无阈值滤波算法。该过滤器是基于点云被视为高斯模型的混合物的假设。因此，从点云中分离地面点和非地面点是通过用于筛选地面点的混合高斯模型对点云进行分区。利用EM算法实现分离，计算混合参数的极大似然估计。使用估计的参数，属于地面或非地面的每个点的似然度计算。值得注意的是，点云被标记为具有较大可能性的组件。所提出的方法已经使用ISPRS提供的标准过滤数据集进行了测试。实验结果表明，与经典的渐进不规则三角网加密（PTD）和基于段的PTD方法相比，该方法在遗漏误差方面表现最好。该方法的平均遗漏误差分别比经典PTD方法和基于片段的PTD方法低52.81%和16.78%。此外，该方法是能够减少其平均总误差的31.95%，相比经典的PTD方法。
Filtering of ground points is a key step for most applications of airborne LiDAR point clouds. Although many filtering algorithms have been proposed in recent years, most of them suffer from parameter setting or thresholds fine-tuning. This is most often time-consuming and reduces the degree of automation of the applied algorithm. To overcome such problems, this paper proposes a threshold-free filtering algorithm based on expectation–maximization (EM). The filter is developed based on the assumption that point clouds are seen as a mixture of Gaussian models. Thus, the separation of ground points and non-ground points from point clouds is partitioning of the point clouds by a mixed Gaussian model that is used for screening ground points. EM is applied to realize the separation, which calculates the maximum likelihood estimates of the mixture parameters. Using the estimated parameters, the likelihoods of each point belonging to ground or non-ground are computed. Noticeably, point clouds are labeled as the component with a larger likelihood. The proposed method has been tested using the standard filtering datasets provided by the ISPRS. Experimental results showed that the proposed method performed the best in comparison with the classic progressive triangulated irregular network densification (PTD) and segment-based PTD methods in terms of omission error. The average omission error of the proposed method was 52.81% and 16.78% lower than the classic PTD method and the segment-based PTD method, respectively. Moreover, the proposed method was able to reduce its average total error by 31.95% compared to the classic PTD method.