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Therefore, point-cloud denoising is a key step in point-cloud data processing of photon-counting LiDAR. To explore the adaptability of different denoising algorithms for photon-counting LiDAR data in different times and spaces, in this paper, NASA\u2019s official differential, regressive and Gaussian adaptive nearest neighbor (DRAGANN) algorithm; Herzfeld\u2019s radial basis function (RBF) denoising algorithm; and the density-based spatial clustering of applications with noise (DBSCAN) algorithm based on density clustering are used to denoise the ICESat-2 ATL03 photon point-cloud data. Airborne LiDAR data are used to verify the denoising accuracy, and then the adaptability of the three algorithms is discussed. The results show that the DRAGANN algorithm is suitable for data with moderate Fraction Vegetation Coverage (FVC) (45\u201375%) at night and is less affected by slope; therefore, it is not limited to terrain slope. The denoising accuracy of the RBF algorithm decreases with increasing FVC and decreases with increasing slope. It is suitable for data with low terrain slope (0~55\u00b0) and low FVC (0~220\u00b0), which is less affected by observation time; therefore, it is suitable for all-day data. The DBSCAN algorithm is suitable for data with moderate FVC (45~75%) at night, regardless of terrain slope. Unlike the DRAGANN algorithm, the DBSCAN algorithm is greatly affected by solar noise photons, but at night, its denoising accuracy is higher than that of the DRAGANN algorithm. The research results have certain reference significance for the subsequent processing and application of ICESat-2 data.<\/jats:p>","DOI":"10.3390\/rs15153884","type":"journal-article","created":{"date-parts":[[2023,8,5]],"date-time":"2023-08-05T10:25:36Z","timestamp":1691231136000},"page":"3884","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Research on the Adaptability of Typical Denoising Algorithms Based on ICESat-2 Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Mengyun","family":"Kui","sequence":"first","affiliation":[{"name":"Faculty of Geography, Yunnan Normal University, Kunming 650500, China"},{"name":"Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan Kunming, Kunming 650500, China"},{"name":"Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China"}]},{"given":"Yunna","family":"Xu","sequence":"additional","affiliation":[{"name":"Faculty of Geography, Yunnan Normal University, Kunming 650500, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7202-646X","authenticated-orcid":false,"given":"Jinliang","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Geography, Yunnan Normal University, Kunming 650500, China"},{"name":"Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan Kunming, Kunming 650500, China"},{"name":"Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China"}]},{"given":"Feng","family":"Cheng","sequence":"additional","affiliation":[{"name":"Faculty of Geography, Yunnan Normal University, Kunming 650500, China"},{"name":"Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan Kunming, Kunming 650500, China"},{"name":"Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,5]]},"reference":[{"key":"ref_1","first-page":"127","article-title":"The Basic Principle And Development Of LiDAR Remote Sensing","volume":"2","author":"Sui","year":"2006","journal-title":"J. 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