Journal of Geo-information Science ›› 2020, Vol. 22 ›› Issue (4): 898-908.doi: 10.12082/dqxxkx.2020.190774

Previous Articles     Next Articles

A Terrain-adaptive Airborne LiDAR Point Cloud Filtering Method Using Regularized TPS

ZHANG Yongjun*(), HUANG Xingbei, LIU Xinyi   

  1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
  • Received:2019-12-16 Revised:2020-02-10 Online:2020-04-25 Published:2020-06-10
  • Contact: ZHANG Yongjun E-mail:zhangyj@whu.edu.cn
  • Supported by:
    National Key Research and Development Program of China(2017YFB0503004);National Natural Science Foundation of China(41871368)

Abstract:

With the continuous development of LiDAR technology, the research of LIDAR point cloud data processing is also in-depth. Point cloud filtering is one of the key steps in airborne LiDAR point cloud processing. The existing point cloud filtering algorithms often work well on some specific terrains, however, their filtering results are not satisfying in the cases of undulating terrains or mixed terrains, some post-processing measures are always needed. Based on relative coefficient of variation and regularized thin-plate-spline interpolation, a new terrain adaptive point cloud filtering method is proposed in this paper. The initial seed points are obtained by two-dimensional and 8-directional region-growing method, and then optimized by extracting line features from the point clouds, the points with low reliability are removed from the sets of reference points. After that the reference points are mostly reliable and scattered in the whole test area, and could be used to generate classifying surface. Finally, the classifying surface between ground points and non-ground points is fitted using thin-plate-spline interpolation. Classifying surface is used to absorb more ground points from point cloud, which could provide reference information for the next round of interpolation. In this process we use regularization item of adaptive coefficient to control the bending extent of classifying surface, in order to make the filtering algorithm adaptive to different types of terrains. Ground points are totally filtered after several iterations. The experimental results on point clouds from multiple devices show that the total errors of our proposed method were 4.14% and 4.17% in Guangzhou and ISPRS datasets, respectively. The result of the proposed filtering method is not the best, but it is more stable and has better terrain adaptability compared to state-of-the-art popular algorithms such as progressive TIN filter, cloth simulation filter, semi-global filter, etc. The proposed method outperforms other comparison methods in both error rate and overall performance in several complex or special terrains, as well as high computational efficiency. Additionally, the promising experimental results demonstrate that the proposed adaptive terrain filtering method is an accurate and efficient solution for airborne LiDAR point cloud filtering in complex terrains, such as slopes, ridges, and mixed terrains including vegetation and buildings.

Key words: airborne LiDAR, point cloud filtering, region-growing, reference points optimization, multi-directional scanning, thin plate spline interpolation, regularization, relative coefficient of variation