{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T15:49:32Z","timestamp":1774367372396,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T00:00:00Z","timestamp":1698624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42201489"],"award-info":[{"award-number":["42201489"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["23XD1404100"],"award-info":[{"award-number":["23XD1404100"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22511102900"],"award-info":[{"award-number":["22511102900"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Academic Research Leader Program","award":["42201489"],"award-info":[{"award-number":["42201489"]}]},{"name":"Shanghai Academic Research Leader Program","award":["23XD1404100"],"award-info":[{"award-number":["23XD1404100"]}]},{"name":"Shanghai Academic Research Leader Program","award":["22511102900"],"award-info":[{"award-number":["22511102900"]}]},{"name":"Shanghai Science and Technology Innovation Action Plan Program","award":["42201489"],"award-info":[{"award-number":["42201489"]}]},{"name":"Shanghai Science and Technology Innovation Action Plan Program","award":["23XD1404100"],"award-info":[{"award-number":["23XD1404100"]}]},{"name":"Shanghai Science and Technology Innovation Action Plan Program","award":["22511102900"],"award-info":[{"award-number":["22511102900"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Due to atmospheric scattering, solar radiation, and other factors, the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) product data suffer from a substantial amount of background noise. This poses a significant challenge when attempting to directly utilize the raw data. Consequently, data denoising becomes an indispensable preprocessing step for its subsequent applications, such as the extraction of forest structure parameters and ground elevation data. While the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is currently the most widely used method, it remains susceptible to complexities arising from terrain, low signal-to-noise ratio (SNR), and input parameter variations. This paper proposes an efficient Multi-Level Auto-Adaptive Noise Filter (MLANF) algorithm based on photon spatial density. Its purpose is to extract signal photons from ICESat-2 terrestrial data of different ground cover types. The algorithm follows a two-step process. Firstly, random noise photons are removed from the upper and lower regions of the signal photons through a coarse denoising process. Secondly, in the fine denoising step, the K-Nearest Neighbor (KNN) algorithm selects the K photons to calculate the slope along the track. The calculated slope is then used to rotate the direction of the searching neighborhood in the DBSCAN algorithm. The proposed algorithm was tested in eight datasets of four surface types: forest, grassland, desert, and urban, and the extraction results were compared with those from the ATL08 datasets and the DBSCAN algorithm. Based on the ground-truth signal photons obtained by visual inspection, the classification precision, recall, and F-score of our algorithm, as well as two other algorithms, were calculated. The MLANF could achieve a good balance between classification precision (97.48% averaged) and recall (97.96% averaged). Its F-score (97.69% averaged) was higher than that of the other two methods. This demonstrates that the MLANF algorithm successfully obtained a continuous surface profile from ICESat-2 datasets with different surface cover types, significant topographic relief, and low SNR.<\/jats:p>","DOI":"10.3390\/rs15215176","type":"journal-article","created":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T13:20:07Z","timestamp":1698672007000},"page":"5176","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Multi-Level Auto-Adaptive Noise-Filtering Algorithm for Land ICESat-2 Photon-Counting Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Jun","family":"Liu","sequence":"first","affiliation":[{"name":"College of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China"}]},{"given":"Jingyun","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China"}]},{"given":"Huan","family":"Xie","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration, College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}]},{"given":"Dan","family":"Ye","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration, College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1671-4297","authenticated-orcid":false,"given":"Peinan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Environmental Science and Engineering, Donghua University, Shanghai 201620, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.1016\/j.rse.2019.111325","article-title":"The Ice, Cloud, and Land Elevation Satellite\u20142 mission: A global geolocated photon product derived from the Advanced Topographic Laser Altimeter System","volume":"233","author":"Neumann","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_2","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":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tang, H., Swatantran, A., Barrett, T., DeCola, P., and Dubayah, R. (2016). Voxel-based spatial filtering method for canopy height retrieval from airborne single-photon lidar. Remote Sens., 8.","DOI":"10.3390\/rs8090771"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"111352","DOI":"10.1016\/j.rse.2019.111352","article-title":"Land ice height-retrieval algorithm for NASA\u2019s ICESat-2 photon-counting laser altimeter","volume":"233","author":"Smith","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.rse.2016.12.029","article-title":"The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): Science requirements, concept, and implementation","volume":"190","author":"Markus","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2016.05.011","article-title":"Testing the ice-water discrimination and freeboard retrieval algorithms for the ICESat-2 mission","volume":"183","author":"Kwok","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, J., Xie, H., Guo, Y., Tong, X., and Li, P. (2022). A Sea Ice Concentration Estimation Methodology Utilizing ICESat-2 Photon-Counting Laser Altimeter in the Arctic. Remote Sens., 14.","DOI":"10.3390\/rs14051130"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"112047","DOI":"10.1016\/j.rse.2020.112047","article-title":"Satellite-derived bathymetry using the ICESat-2 lidar and Sentinel-2 imagery datasets","volume":"250","author":"Ma","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ranndal, H., Christiansen, P.S., Kliving, P., Andersen, O.B., and Nielsen, K. (2021). Evaluation of a Statistical Approach for Extracting Shallow Water Bathymetry Signals from ICESat-2 ATL03 Photon Data. Remote Sens., 13.","DOI":"10.3390\/rs13173548"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"L22S02","DOI":"10.1029\/2005GL023971","article-title":"Estimates of forest canopy height and boveground biomass using ICESat","volume":"32","author":"Lefsky","year":"2005","journal-title":"Geophys. Res. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2019.01.037","article-title":"Estimating aboveground biomass and forest canopy cover with simulated ICESat-2 data","volume":"224","author":"Narine","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.isprsjprs.2016.04.009","article-title":"Prospects of the ICESat-2 laser altimetry mission for savanna ecosystem structural studies based on airborne simulation data","volume":"118","author":"Gwenzi","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5263","DOI":"10.1080\/01431161.2014.939780","article-title":"Applicability of an automatic surface detection approach to micro-pulse photon-counting lidar altimetry data: Implications for canopy height retrieval from future ICESat-2 data","volume":"35","author":"Moussavi","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","unstructured":"Zhang, J., Kerekes, J., Csatho, B., Schenk, T., and Wheelwright, R. (2014, January 13\u201318). A clustering approach for detection of ground in micropulse photon-counting LiDAR altimeter data. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1080\/2150704X.2019.1682708","article-title":"Forest signal detection for photon counting LiDAR using Random Forest","volume":"11","author":"Chen","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Magruder, L.A., III, M.E.W., Stout, K.D., and Neuenschwander, A.L. (2012, January 23\u201327). Noise filtering techniques for photon-counting ladar data. Proceedings of the Laser Radar Technology and Applications XVII, Baltimore, MA, USA.","DOI":"10.1117\/12.919139"},{"key":"ref_18","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_19","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":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"4411813","DOI":"10.1109\/TGRS.2022.3176982","article-title":"A Density-Based Adaptive Ground and Canopy Detecting Method for ICESat-2 Photon-Counting Data","volume":"60","author":"Xie","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","first-page":"102872","article-title":"Converting along-track photons into a point-region quadtree to assist with ICESat-2-based canopy cover and ground photon detection","volume":"112","author":"Xie","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Liu, X., Ma, Y., Xu, N., Zhang, W., and Li, S. (2021). Signal Photon Extraction Method for Weak Beam Data of ICESat-2 Using Information Provided by Strong Beam Data in Mountainous Areas. Remote Sens., 13.","DOI":"10.3390\/rs13050863"},{"key":"ref_24","unstructured":"Robbins, J., Neumann, T., Kurtz, N., Brunt, K., Bagnardi, M., Hancock, D., and Lee, J. (2022). ICESat-2 Data Comparison User\u2019s Guide for Rel005, Goddard Space Flight Center."},{"key":"ref_25","unstructured":"Neumann, T.A., Brenner, D., Hancock, J., Robbins, J., Saba, K., Harbeck, A., Gibbons, J., Lee, S.B., Luthcke, T., and Rebold, T. (2023, August 27). ATLAS\/ICESat-2 L2A Global Geolocated Photon Data, Version 5. Available online: https:\/\/doi.org\/10.5067\/ATLAS\/ATL03.005."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.rse.2018.11.005","article-title":"The ATL08 land and vegetation product for the ICESat-2 Mission","volume":"221","author":"Neuenschwander","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_27","unstructured":"Neuenschwander, A.L., Pitts, K.L., Jelley, B.P., Robbins, J., Klotz, B., Popescu, S.C., Nelson, R., Harding, D., Pederson, D., and Sheridan, R. (2023, August 27). ATLAS\/ICESat-2 L3A Land and Vegetation Height, Version 5. Available online: https:\/\/doi.org\/10.5067\/ATLAS\/ATL08.005."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7604","DOI":"10.1109\/JSTARS.2021.3094195","article-title":"A Comparison and Review of Surface Detection Methods Using MBL, MABEL, and ICESat-2 Photon-Counting Laser Altimetry Data","volume":"14","author":"Xie","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1197\/jamia.M1733","article-title":"Agreement, the f-measure, and reliability in information retrieval","volume":"12","author":"Hripcsak","year":"2005","journal-title":"J. Am. Med. Inform. Assoc."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/21\/5176\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:14:18Z","timestamp":1760130858000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/21\/5176"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,30]]},"references-count":29,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["rs15215176"],"URL":"https:\/\/doi.org\/10.3390\/rs15215176","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,30]]}}}