A Novel DSM Filtering Algorithm for Landslide Monitoring Based on Multiconstraints

With the aim of addressing the problems of vegetation interference and noise points existing in the process of digital surface model (DSM) filtering for landslide monitoring, this paper presents a point cloud filtering algorithm based on multiconstraints, which include vegetation detection, a multiview constraint, and local terrain continuity. The proposed algorithm is implemented via the following steps. First, we carry out the vegetation detection procedure and then remove the corresponding vegetation regions from the original images. Next, we utilize the multiview and local terrain continuity constraints to eliminate noise points from the generated point clouds (obtained through close-range photogrammetric processing). Finally, we apply the filtered point clouds to the landslide monitoring test to acquire reliable deformation information. The experimental results confirm that the vegetation and noise points can be effectively removed from the original point clouds by the use of the filtering algorithm proposed in this paper. Furthermore, accurate deformation areas can be obtained by comparing the DSMs from the different periods, and the achieved result is very close to the actual situation, which proves the effectiveness of the proposed algorithm.