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

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...

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Vydané v:Earth, planets, and space Ročník 76; číslo 1; s. 128 - 16
Hlavní autori: Wang, Dong, Yu, Jiachen, Liu, Fengying, Li, Qinghua
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024
Springer
Springer Nature B.V
SpringerOpen
Predmet:
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
China
DBSCAN
ICESat-2
Distance statistics
Laser altimetry
Point cloud denoising
ISSN:1880-5981, 1343-8832, 1880-5981
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Popis
Shrnutí: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
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1880-5981
1343-8832
1880-5981
DOI:10.1186/s40623-024-02071-y