ICESat-2 single photon laser point cloud denoising algorithm based on improved DBSCAN clustering

• Published in: Earth, planets, and space planets, and space, 2024-12, Vol.76 (1), p.128-16, Article 128
• Main Authors: Wang, Dong, Yu, Jiachen, Liu, Fengying, Li, Qinghua
• Format: Article
• Language: English
• Subjects: 6. Geodesy; Algorithms; Analysis; Background noise; Cluster analysis; Clustering; DBSCAN; Digital elevation models; Distance statistics; Earth and Environmental Science; Earth Sciences; Elevation; Energy consumption; Geology; Geophysics/Geodesy; ICESat-2; Laser altimetry; Laser applications; Lasers; Lidar; Mountain regions; Mountainous areas; Mountains; Noise measurement; Noise reduction; Noise sensitivity; Photons; Point cloud denoising; Random noise theory
• ISSN: 1880-5981; 1343-8832; 1880-5981
• DOI: 10.1186/s40623-024-02071-y

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has great potential for development due to its advantages of the use of multiple beams, low energy consumption, high repetition frequency, and high measurement sensitivity. However, the weak photon signal emitted by the photon counting lidar is susceptible to the background noise caused by the sun and the atmosphere, which can seriously affect the processing and application of laser data. This paper proposes an improved DBSCAN clustering algorithm for denoising single photon laser point clouds in mountainous areas. Firstly, a grouping method based on elevation and distance statistics is proposed to reduce the influence of terrain undulations on denoising accuracy. Finally, an automatic radius search method is put forward to determine clustering radius of each group, automatically find the optimal radius, and improve the existing DBSCAN clustering method. The method proposed in this paper is compared with the classical DBSCAN algorithm. The results show that the proposed algorithm significantly improves denoising accuracy in mountainous areas and effectively filters out most background noise. Graphical Abstract