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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Abstract	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
AbstractList	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 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. Abstract 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 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
ArticleNumber	128
Audience	Academic
Author	Yu, Jiachen Li, Qinghua Liu, Fengying Wang, Dong
Author_xml	– sequence: 1 givenname: Dong surname: Wang fullname: Wang, Dong email: sdustwd@163.com organization: College of Geodesy and Geomatics, Shandong University of Science and Technology, National Demonstration Center for Experimental Surveying and Mapping Education (Shandong University of Science and Technology) – sequence: 2 givenname: Jiachen surname: Yu fullname: Yu, Jiachen organization: College of Geodesy and Geomatics, Shandong University of Science and Technology – sequence: 3 givenname: Fengying surname: Liu fullname: Liu, Fengying organization: College of Geodesy and Geomatics, Shandong University of Science and Technology, National Demonstration Center for Experimental Surveying and Mapping Education (Shandong University of Science and Technology) – sequence: 4 givenname: Qinghua surname: Li fullname: Li, Qinghua organization: College of Geodesy and Geomatics, Shandong University of Science and Technology
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Cites_doi	10.1109/TGRS.2021.3051086 10.1051/epjconf/202023701003 10.1029/2020EA001235 10.1029/2020EA001491 10.1007/s11771-022-4896-x 10.1016/j.jhydrol.2021.127257 10.1109/JSTARS.2021.3094195 10.1029/2021GL097448 10.1016/j.rse.2020.112110 10.3390/rs12172827 10.3390/info12060235 10.3390/RS15174260 10.1016/j.rse.2021.112711 10.5555/3001460.3001507 10.3390/RS15153884 10.1109/JSTARS.2022.3189042 10.1364/ao.469584 10.1016/j.rse.2019.111325 10.3390/rs10121962 10.3788/IRLA20200259 10.1109/LGRS.2020.3003191 10.1109/LGRS.2021.3081721 10.1016/j.rse.2021.112571 10.1029/2020EA001538 10.1016/j.rse.2022.112919 10.1016/j.jag.2020.102163 10.3390/rs13050863 10.1109/LGRS.2016.2555308 10.3390/rs14051185 10.3390/rs12132084
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Snippet	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... Abstract 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...
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SubjectTerms	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
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Title	ICESat-2 single photon laser point cloud denoising algorithm based on improved DBSCAN clustering
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