Segments-based progressive TIN densification filter for DTM generation from airborne LIDAR data

Airborne light detection and ranging (LIDAR) has revolutionized conventional methods for digital terrain models (DTMs) acquisition.Ground filtering for airborne LIDAR is one of the core steps taken to obtain a high quality DTM.This paper presents a segments-based progressive TIN (triangulated irregular network) densification (SPTD) filter that can automatically separate ground points from non-ground points.The SPTD method is composed of two key steps:point cloud segmentation and clustering by iterative judgement.The clustering method uses the dual distance to obtain a set of seed points as a coarse spatial clustering process.Then the rest of the valid point clouds are classified iteratively.Finally,the datasets provided by ISPRS are utilized to test the filtering performance.In comparison with the commercial software TerraSolid,the experimental results show that the SPTD method in this paper can avoid single threshold restrictions.The expected accuracy of ground point determination is capable of producing reliable DTMs in the discontinuous areas.