Interpolation-based filtering with segmentation for airborne LiDAR point clouds

The classical airborne LiDAR filtering algorithms show good results on most landscapes, but they suffer from low-level adaptability on steep slopes. Thus, to improve the filtering performance under different environments, a surface interpolation-based filtering algorithm with segmentation was proposed. Firstly, the original point clouds were grouped into a set of segments and one set of scattered points by an improved region growing method. Then, the segments and the scattered points were classified simultaneously using a weighted least square algorithm. The benchmark dataset provided by International Society for Photogrammetry and Remote Sensing(ISPRS) was used to validate the performance of the proposed method. Results illustrate that the proposed method outperforms the state-of-the-art filtering methods on 11 out of 15 samples, showing its strong adaptability to different terrain environments. Moreover, the proposed method has the lowest average total error. Filtering three samples of high-density with different terrain features also demonstrates the promising performance of the proposed method.