Automatic DTM extraction from airborne LiDAR based on expectation-maximization

Abstract

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.