An adaptive filtering algorithm of multilevel resolution point cloud

Abstract

The existing filtering methods for airborne LiDAR point cloud have low accuracy. An adaptive filtering algorithm is proposed which is improved based on multilevel resolution algorithm. First double index structure of Octree and KDtree is established. Then the initial reference surface is constructed by ground seed points. According to the slope fluctuation situation, the grid resolution of the ground referential surface is adjusted in an adaptive way. Finally, the refined surface is formed gradually by multilevel renewing resolution to provide filtered point cloud with high accuracy. Experimental results show that the error of Type II can be effectively reduced, the average Kappa coefficient increases by 0.53% and the average total error decreases by 0.44% compared with multiresolution hierarchical classification algorithm. The result tested by practically measured data shows that Kappa coefficient can reach 90%. Especially, it maintains advantages of high accuracy under complex topographic environment.

Keywords:

• Adaptive filtering
• Airborne LiDAR
• Double index
• Oblique angle
• Multilevel resolution

Acknowledgements

We would like to thank International Society for Photogrammetry and Remote Sensing (http://www.itc.nl/isprswgIII-3/filtertest/) for providing the testing data. Special thanks to Chuanfa Chen who gave us much valuable advice. We would also like to thank the constructive comments from the editor and anonymous reviewers.

Additional information

Funding

This work was supported by Natural Science Foundation of Shandong Province, China: [Grant Number ZR2019PD016]; Innovation Training Program for college students of shandong province China: [Grant Number S201910424051]; The Major Science and Technology Innovation Projects of Shandong Province, China: [Grant Number 2019JZZY020103].

Notes on contributors

Youyuan Li

Youyuan Li had the original idea that uses multilevel resolution algorithm to filter LiDAR data in an adaptive way and wrote this manuscript. Jian Wang refined and implemented this idea. Bin Li helped to refine some paramenters settings. Wenxiao Sun and Yanyi Li helped to review this article and gave much advice.