{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T17:08:32Z","timestamp":1770916112850,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T00:00:00Z","timestamp":1693353600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42261063"],"award-info":[{"award-number":["42261063"]}]},{"name":"National Natural Science Foundation of China","award":["41901370"],"award-info":[{"award-number":["41901370"]}]},{"name":"National Natural Science Foundation of China","award":["GuikeAD19110064"],"award-info":[{"award-number":["GuikeAD19110064"]}]},{"name":"National Natural Science Foundation of China","award":["2018GXNSFBA28107"],"award-info":[{"award-number":["2018GXNSFBA28107"]}]},{"name":"National Natural Science Foundation of China","award":["Hongchang He"],"award-info":[{"award-number":["Hongchang He"]}]},{"name":"Guangxi Science and Technology Base and Talent Project","award":["42261063"],"award-info":[{"award-number":["42261063"]}]},{"name":"Guangxi Science and Technology Base and Talent Project","award":["41901370"],"award-info":[{"award-number":["41901370"]}]},{"name":"Guangxi Science and Technology Base and Talent Project","award":["GuikeAD19110064"],"award-info":[{"award-number":["GuikeAD19110064"]}]},{"name":"Guangxi Science and Technology Base and Talent Project","award":["2018GXNSFBA28107"],"award-info":[{"award-number":["2018GXNSFBA28107"]}]},{"name":"Guangxi Science and Technology Base and Talent Project","award":["Hongchang He"],"award-info":[{"award-number":["Hongchang He"]}]},{"name":"Guangxi Natural Science Foundation","award":["42261063"],"award-info":[{"award-number":["42261063"]}]},{"name":"Guangxi Natural Science Foundation","award":["41901370"],"award-info":[{"award-number":["41901370"]}]},{"name":"Guangxi Natural Science Foundation","award":["GuikeAD19110064"],"award-info":[{"award-number":["GuikeAD19110064"]}]},{"name":"Guangxi Natural Science Foundation","award":["2018GXNSFBA28107"],"award-info":[{"award-number":["2018GXNSFBA28107"]}]},{"name":"Guangxi Natural Science Foundation","award":["Hongchang He"],"award-info":[{"award-number":["Hongchang He"]}]},{"name":"BaGuiScholars program of the provincial government of Guangxi","award":["42261063"],"award-info":[{"award-number":["42261063"]}]},{"name":"BaGuiScholars program of the provincial government of Guangxi","award":["41901370"],"award-info":[{"award-number":["41901370"]}]},{"name":"BaGuiScholars program of the provincial government of Guangxi","award":["GuikeAD19110064"],"award-info":[{"award-number":["GuikeAD19110064"]}]},{"name":"BaGuiScholars program of the provincial government of Guangxi","award":["2018GXNSFBA28107"],"award-info":[{"award-number":["2018GXNSFBA28107"]}]},{"name":"BaGuiScholars program of the provincial government of Guangxi","award":["Hongchang He"],"award-info":[{"award-number":["Hongchang He"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), which can achieve high-precision ground detection. However, due to its low pulse energy and high sensitivity, it is also affected by noise when obtaining data. Therefore, data denoising is critical to the subsequent processing and application. In this study, a multilevel filtering algorithm is proposed to denoise the daytime photon cloud data with high background noise. Firstly, the Random Sample Consensus (RANSAC) algorithm is used to roughly denoise the daytime photon cloud data with high background noise, and a signal point cloud buffer is established to remove most of the noise points. Subsequently, the horizontal continuity parameter is calculated based on the photon cloud data following the rough denoising process. Based on this parameter, the shape and size of the search domain of the results of the subsequent fine denoising algorithm are adaptively improved. Finally, three filtering directions (a horizontal direction, an intra-group unified direction, and an adaptive direction for each photon) are proposed, and a hybrid algorithm combining the Ordering Points to Identify the Clustering Structure (OPTICS) density clustering algorithm and the Relative Neighboring Relationship K-nearest neighbors-based noise removal (RNR\u2212KNNB) algorithm is employed to accurately denoise the photon cloud data in the three filtering directions. Furthermore, the RANSAC algorithm based on a sliding overlap window is used to remove outliers for the weak beam fine denoising photon cloud data. The results indicate that, for the strong beams, the denoising accuracy of the multilevel filtering algorithm in the three filtering directions is comparable (Rs \u2265 0.96, F \u2265 0.67), and they are all better than that of the ATL08 algorithm (Rs\/Rn\/p\/F = 0.85\/0.67\/0.52\/0.65). For weak beams, the denoising accuracy of the multilevel filtering algorithm in the horizontal direction and the intra-group unified direction is similar (Rs = 0.92, F = 0.69), and it is superior to the denoising results in the adaptive direction of each photon and the ATL08 algorithm (Rs\/Rn\/p\/F = 0.94\/0.84\/0.51\/0.65, 0.88\/0.87\/0.55\/0.67, respectively). For strong and weak beams, the p-value and F-value of the denoising results of multilevel filtering algorithms in three different filtering directions increase with the increase of SNR value. It is demonstrated that SNR is an important factor affecting the denoising results of algorithms. The multilevel filtering algorithm proposed in the study can effectively achieve precise denoising of daytime photon cloud data with high background noise, and the three different filtering directions have different effects on the denoising results of strong and weak beam photon cloud data. This can provide technical and methodological guidance for subsequent photon cloud data filtering processing.<\/jats:p>","DOI":"10.3390\/rs15174260","type":"journal-article","created":{"date-parts":[[2023,8,30]],"date-time":"2023-08-30T10:09:49Z","timestamp":1693390189000},"page":"4260","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Research on Multilevel Filtering Algorithm Used for Denoising Strong and Weak Beams of Daytime Photon Cloud Data with High Background Noise"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9881-949X","authenticated-orcid":false,"given":"Haotian","family":"You","sequence":"first","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, No. 12 Jiangan Street, Guilin 541006, China"}]},{"given":"Yuecan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, No. 12 Jiangan Street, Guilin 541006, China"}]},{"given":"Zhigang","family":"Qin","sequence":"additional","affiliation":[{"name":"Hechi Meteorological Bureau, No. 298 Jincheng Middle Road, Hechi 547099, China"}]},{"given":"Peng","family":"Lei","sequence":"additional","affiliation":[{"name":"The School of Hydraulic Engineering, Guangxi Vocational College of Water Resources and Electric Power, No. 99 Chang\u2019gu Road, Nanning 530023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9464-3442","authenticated-orcid":false,"given":"Jianjun","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, No. 12 Jiangan Street, Guilin 541006, China"}]},{"given":"Xue","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, No. 12 Jiangan Street, Guilin 541006, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1109\/LGRS.2020.3003191","article-title":"A noise removal algorithm based on OPTICS for photon-counting LiDAR data","volume":"18","author":"Zhu","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","unstructured":"Ranson, K.J., Sun, G., Kovacs, K., and Kharuk, V.I. (2004, January 20\u201324). Landcover attributes from ICESat GLAS data in central Siberia. Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.rse.2019.02.017","article-title":"Slope-adaptive waveform metrics of large footprint lidar for estimation of forest aboveground biomass","volume":"224","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.isprsjprs.2018.11.027","article-title":"Measuring stem diameters with TLS in boreal forests by complementary fitting procedure","volume":"147","author":"Raumonen","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Vernimmen, R., Hooijer, A., and Pronk, M. (2020). New ICESat-2 satellite LiDAR data allow first global lowland DTM suitable for accurate coastal flood risk assessment. Remote Sens., 12.","DOI":"10.3390\/rs12172827"},{"key":"ref_6","first-page":"1199","article-title":"Point cloud filtering and tree height estimation using airborne experiment data of ICESat-2","volume":"18","author":"Xia","year":"2014","journal-title":"J. Remote Sens"},{"key":"ref_7","first-page":"25","article-title":"Implementation and accuracy evaluation of ICESat-2 ATL08 denoising algorithms","volume":"25","author":"Bincai","year":"2020","journal-title":"Bull. Surv. Mapp."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2018.02.019","article-title":"Photon counting LiDAR: An adaptive ground and canopy height retrieval algorithm for ICESat-2 data","volume":"208","author":"Popescu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2109","DOI":"10.1109\/TGRS.2013.2258350","article-title":"Algorithm for detection of ground and canopy cover in micropulse photon-counting lidar altimeter data in preparation for the ICESat-2 mission","volume":"52","author":"Herzfeld","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","first-page":"363","article-title":"An adaptive directional model for estimating vegetation canopy height using space-borne photon counting laser altimetry data","volume":"39","author":"Wang","year":"2020","journal-title":"J. Infrared Millim. Waves"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1364\/AO.47.000296","article-title":"Acquisition algorithm for direct-detection ladars with Geiger-mode avalanche photodiodes","volume":"47","author":"Milstein","year":"2008","journal-title":"Appl. Opt."},{"key":"ref_12","first-page":"2086","article-title":"Denoising and Classification of ICESat-2 Photon Point Cloud based on Convolutional Neural Network","volume":"23","author":"Lu","year":"2021","journal-title":"J. Geol. Inf. Sci."},{"key":"ref_13","unstructured":"Zhu, X. (2021). Forest Height Retrieval of China with a Resolution of 30 m Using ICESat-2 and GEDI Data, University of the Chinese Academy of Sciences."},{"key":"ref_14","unstructured":"Magruder, L.A., Wharton, M.E., Stout, K.D., and Neuenschwander, A.L. (2012). Laser Radar Technology and Applications XVII, SPIE."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.isprsjprs.2023.06.009","article-title":"Satellite-derived sediment distribution mapping using ICESat-2 and SuperDove","volume":"202","author":"Zhang","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Huang, J., Xing, Y., You, H., Qin, L., Tian, J., and Ma, J. (2019). Particle swarm optimization-based noise filtering algorithm for photon cloud data in forest area. Remote Sens., 11.","DOI":"10.3390\/rs11080980"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1891","DOI":"10.1109\/LGRS.2020.3011215","article-title":"A filtering method for ICESat-2 photon point cloud data based on relative neighboring relationship and local weighted distance statistics","volume":"18","author":"Li","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhu, X., Nie, S., Wang, C., Xi, X., and Hu, Z. (2018). A ground elevation and vegetation height retrieval algorithm using micro-pulse photon-counting lidar data. Remote Sens., 10.","DOI":"10.3390\/rs10121962"},{"key":"ref_19","first-page":"271","article-title":"Study on canopy height estimation based on ICESat-2\/ATLAS photon-counting LiDAR data","volume":"73","author":"Huang","year":"2021","journal-title":"J. Northeast For. Univ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3303839","article-title":"Forest Canopy Height Extraction Method Based on ICESat-2\/ATLAS Data","volume":"61","author":"Huang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Neuenschwander, A.L., and Magruder, L.A. (2019). Canopy and terrain height retrievals with ICESat-2: A first look. Remote Sens., 11.","DOI":"10.3390\/rs11141721"},{"key":"ref_22","first-page":"164","article-title":"Accuracy of photon cloud noise filtering algorithm in forest area under weak beam conditions","volume":"51","author":"Yanqiu","year":"2020","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_23","unstructured":"Neuenschwander, A., Pitts, K., Jelley, B., Robbins, J., Markel, J., Popescu, S., Nelson, R., Harding, D., Pederson, D., and Klotz, B. (2019). Ice, Cloud, and Land Elevation Satellite 2 (ICESat-2) Algorithm Theoretical Basis Document (ATBD) for Land-Vegetation Along-Track Products (ATL08)."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Carrasco, L., Giam, X., Pape\u015f, M., and Sheldon, K.S. (2019). Metrics of lidar-derived 3D vegetation structure reveal contrasting effects of horizontal and vertical forest heterogeneity on bird species richness. Remote Sens., 11.","DOI":"10.3390\/rs11070743"},{"key":"ref_25","unstructured":"Wang, Y. (2020). Signal Processing on Spaceborne Photon Counting Laser Point Cloud and its Applications in Vegetation Remote Sensing. Wuhan Univ., 3."},{"key":"ref_26","first-page":"1","article-title":"A Novel Noise Filtering Evaluation Criterion of ICESat-2 Signal Photon Data in Forest Environments","volume":"19","author":"Huang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","first-page":"3067609","article-title":"A Self-Adaptive Denoising Algorithm Based on Genetic Algorithm for Photon-Counting Lidar Data","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","first-page":"103387","article-title":"A methodological framework for specular return removal from photon-counting LiDAR data","volume":"122","author":"Wang","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","first-page":"1476","article-title":"Adaptive denoising and classification algorithms for ICESat-2 airborne experimental photon cloud data of 2018","volume":"24","author":"Qin","year":"2020","journal-title":"J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1080\/17538947.2023.2203952","article-title":"A new denoising method for photon-counting LiDAR data with different surface types and observation conditions","volume":"16","author":"Lao","year":"2023","journal-title":"Int. J. Digit. Earth"},{"key":"ref_31","first-page":"3267823","article-title":"A Photon Cloud Filtering Method in Forested Areas Considering the Density Difference Between Canopy Photons and Ground Photons","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1109\/LGRS.2014.2360367","article-title":"An adaptive density-based model for extracting surface returns from photon-counting laser altimeter data","volume":"12","author":"Zhang","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"8395","DOI":"10.1364\/AO.469584","article-title":"ICESat-2 laser data denoising algorithm based on a back propagation neural network","volume":"61","author":"Meng","year":"2022","journal-title":"Appl. Opt."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhu, J., Fu, H., Gao, S., and Wang, C. (2022). Filtering Photon Cloud Data in Forested Areas Based on Elliptical Distance Parameters and Machine Learning Approach. Forests, 13.","DOI":"10.3390\/f13050663"},{"key":"ref_35","first-page":"107","article-title":"An adaptive directional filter for photon counting Lidar point cloud data","volume":"36","author":"Xie","year":"2017","journal-title":"J. Infrared Millim. Waves"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3083897","article-title":"On the hardness and approximation of Euclidean DBSCAN","volume":"42","author":"Gan","year":"2017","journal-title":"ACM Trans. Database Syst. TODS"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4260\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:43:03Z","timestamp":1760128983000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/17\/4260"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,30]]},"references-count":36,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["rs15174260"],"URL":"https:\/\/doi.org\/10.3390\/rs15174260","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,30]]}}}