{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:43:40Z","timestamp":1760147020543,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T00:00:00Z","timestamp":1672617600000},"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":["42071355","41871291","41871314","K2021G027"],"award-info":[{"award-number":["42071355","41871291","41871314","K2021G027"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Project of Science and Technology Research and Development Plan of China National Railway Group Co., Ltd.","award":["42071355","41871291","41871314","K2021G027"],"award-info":[{"award-number":["42071355","41871291","41871314","K2021G027"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Compared with the existing modes of LiDAR, single-photon LiDAR (SPL) can acquire terrain data more efficiently. However, influenced by the photon-sensitive detectors, the collected point cloud data contain a large number of noisy points. Most of the existing denoising techniques are based on the sparsity assumption of point cloud noise, which does not hold for SPL point clouds, so the existing denoising methods cannot effectively remove the noisy points from SPL point clouds. To solve the above problems, we proposed a novel multistage denoising strategy with fused multiscale features. The multiscale features were fused to enrich contextual information of the point cloud at different scales. In addition, we utilized multistage denoising to solve the problem that a single-round denoising could not effectively remove enough noise points in some areas. Interestingly, the multiscale features also prevent an increase in false-alarm ratio during multistage denoising. The experimental results indicate that the proposed denoising approach achieved 97.58%, 99.59%, 95.70%, and 77.92% F1-scores in the urban, suburban, mountain, and water areas, respectively, and it outperformed the existing denoising methods such as Statistical Outlier Removal. The proposed approach significantly improved the denoising precision of airborne point clouds from single-photon LiDAR, especially in water areas and dense urban areas.<\/jats:p>","DOI":"10.3390\/rs15010269","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T04:56:27Z","timestamp":1672635387000},"page":"269","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multiscale Feature Fusion for the Multistage Denoising of Airborne Single Photon LiDAR"],"prefix":"10.3390","volume":"15","author":[{"given":"Shuming","family":"Si","sequence":"first","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1137-2208","authenticated-orcid":false,"given":"Han","family":"Hu","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Yulin","family":"Ding","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Xuekun","family":"Yuan","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Ying","family":"Jiang","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Yigao","family":"Jin","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Xuming","family":"Ge","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}]},{"given":"Yeting","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China"}]},{"given":"Jie","family":"Chen","sequence":"additional","affiliation":[{"name":"China Academy of Railway Sciences Group Co., Ltd., Beijing 100081, China"}]},{"given":"Xiaocui","family":"Guo","sequence":"additional","affiliation":[{"name":"Beijing Jingwei Information Technology Co., Ltd., Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ye, Z., Xu, Y., Huang, R., Tong, X., Li, X., Liu, X., Luan, K., Hoegner, L., and Stilla, U. (2020). Lasdu: A large-scale aerial lidar dataset for semantic labeling in dense urban areas. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9070450"},{"key":"ref_2","first-page":"102728","article-title":"Evaluating the effect of DEM resolution on performance of cartographic depth-to-water maps, for planning logging operations","volume":"108","author":"Mohtashami","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jovanovi\u0107, D., Milovanov, S., Ruskovski, I., Govedarica, M., Sladi\u0107, D., Radulovi\u0107, A., and Paji\u0107, V. (2020). Building virtual 3D city model for Smart Cities applications: A case study on campus area of the University of Novi Sad. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9080476"},{"key":"ref_4","first-page":"102327","article-title":"Fusion of crown and trunk detections from airborne UAS based laser scanning for small area forest inventories","volume":"100","author":"Kukkonen","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_5","first-page":"102163","article-title":"High-resolution mapping of forest canopy height using machine learning by coupling ICESat-2 LiDAR with Sentinel-1, Sentinel-2 and Landsat-8 data","volume":"92","author":"Li","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s13595-021-01100-0","article-title":"Estimation of breast height diameter and trunk curvature with linear and single-photon LiDARs","volume":"78","author":"Ahola","year":"2021","journal-title":"Ann. For. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"455","DOI":"10.14358\/PERS.82.7.455","article-title":"First Evaluation on Single Photon-Sensitive Lidar Data","volume":"82","author":"Li","year":"2016","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Brown, R., Hartzell, P., and Glennie, C. (2020). Evaluation of SPL100 Single Photon Lidar Data. Remote Sens., 12.","DOI":"10.3390\/rs12040722"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, X., Glennie, C., and Pan, Z. (2017, January 23\u201328). Adaptive noise filtering for single photon Lidar observations. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127718"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Stoker, J.M., Abdullah, Q.A., Nayegandhi, A., and Winehouse, J. (2016). Evaluation of Single Photon and Geiger Mode Lidar for the 3D Elevation Program. Remote Sens., 8.","DOI":"10.3390\/rs8090767"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"112169","DOI":"10.1016\/j.rse.2020.112169","article-title":"Evaluating the capacity of single photon lidar for terrain characterization under a range of forest conditions","volume":"252","author":"White","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Degnan, J.J. (2016). Scanning, Multibeam, Single Photon Lidars for Rapid, Large Scale, High Resolution, Topographic and Bathymetric Mapping. Remote Sens., 8.","DOI":"10.3390\/rs8110958"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2011.10.002","article-title":"Parameter-free ground filtering of LiDAR data for automatic DTM generation","volume":"67","author":"Mongus","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Itzler, M.A., Entwistle, M., Wilton, S., Kudryashov, I., Kotelnikov, J., Piccione, B., Owens, M., and Rangwala, S. (2017, January 26\u201329). Geiger-Mode LiDAR: From Airborne Platforms To Driverless Cars. Proceedings of the Applied Industrial Optics: Spectroscopy, Imaging and Metrology, San Francisco, CA, USA.","DOI":"10.1364\/AIO.2017.ATu3A.3"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107477","DOI":"10.1016\/j.optlastec.2021.107477","article-title":"Multi-beam single-photon LiDAR with hybrid multiplexing in wavelength and time","volume":"145","author":"Wu","year":"2022","journal-title":"Opt. Laser Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"307","DOI":"10.14358\/PERS.82.5.307","article-title":"A star is born: The state of new lidar technologies","volume":"82","author":"Abdullah","year":"2016","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_17","unstructured":"Leica (2017, February 17). Leica SPL100 Single Photon LiDAR Sensor Data Sheet. Available online: https:\/\/leica-geosystems.com\/en-us\/products\/airborne-systems\/topographic-lidar-sensors\/leica-spl100."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"397","DOI":"10.5194\/isprs-annals-IV-2-W5-397-2019","article-title":"A comparison of single photon and full waveform LiDAR","volume":"4","author":"Mandlburger","year":"2019","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mandlburger, G., and Jutzi, B. (2019). On the Feasibility of Water Surface Mapping with Single Photon LiDAR. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8040188"},{"key":"ref_20","unstructured":"Jutzi, B. (2017, January 11\u201315). Less Photons for More LiDAR? A Review from Multi-Photon Detection to Single Photon Detection. Proceedings of the 56th Photogrammetric Week (PhoWo 2017), Stuttgart, Germany."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"28277","DOI":"10.1038\/srep28277","article-title":"Rapid, High-Resolution Forest Structure and Terrain Mapping over Large Areas using Single Photon Lidar","volume":"6","author":"Swatantran","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, X., Glennie, C., and Pan, Z. (2018). Weak Echo Detection from Single Photon Lidar Data Using a Rigorous Adaptive Ellipsoid Searching Algorithm. Remote Sens., 10.","DOI":"10.3390\/rs10071035"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1016\/j.asr.2015.06.039","article-title":"Single photon laser altimeter data processing, analysis and experimental validation","volume":"56","author":"Vacek","year":"2015","journal-title":"Adv. Space Res."},{"key":"ref_24","first-page":"96711S","article-title":"A denoising approach for detection of canopy and ground from ICESat-2\u2019s airborne simulator data in Maryland, USA","volume":"Volume 9671","author":"Chen","year":"2015","journal-title":"AOPC 2015: Advances in Laser Technology and Applications"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e7325600","DOI":"10.1155\/2021\/7325600","article-title":"Optimization Algorithm for Point Cloud Quality Enhancement Based on Statistical Filtering","volume":"2021","author":"Zhao","year":"2021","journal-title":"J. Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"126567","DOI":"10.1016\/j.optcom.2020.126567","article-title":"Low-complexity point cloud denoising for LiDAR by PCA-based dimension reduction","volume":"482","author":"Duan","year":"2021","journal-title":"Opt. Commun."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhu, L., Cai, X., and Dong, L. (2022, January 8\u20139). Noise removal algorithm based on point cloud classification. Proceedings of the 2022 International Seminar on Computer Science and Engineering Technology (SCSET), Indianapolis, IN, USA.","DOI":"10.1109\/SCSET55041.2022.00030"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.ifacol.2018.11.566","article-title":"Fast Statistical Outlier Removal Based Method for Large 3D Point Clouds of Outdoor Environments","volume":"51","author":"Balta","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1109\/LGRS.2016.2555308","article-title":"A Novel Noise Filtering Model for Photon-Counting Laser Altimeter Data","volume":"13","author":"Wang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lin, R., Hu, H., Wen, Z., and Yin, L. (2021, January 3\u20135). Research on denoising and segmentation algorithm application of pigs\u2019 point cloud based on DBSCAN and PointNet. Proceedings of the 2021 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Trento-Bolzano, Italy.","DOI":"10.1109\/MetroAgriFor52389.2021.9628501"},{"key":"ref_31","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_32","doi-asserted-by":"crossref","unstructured":"Zaman, F., Wong, Y.P., and Ng, B.Y. (2017). Density-Based Denoising of Point Cloud. Proceedings of the 9th International Conference on Robotic, Vision, Signal, Processing and Power Applications, Springer.","DOI":"10.1007\/978-981-10-1721-6_31"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102857","DOI":"10.1016\/j.cad.2020.102857","article-title":"A feature-preserving framework for point cloud denoising","volume":"127","author":"Liu","year":"2020","journal-title":"Comput.-Aided Des."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"278","DOI":"10.5201\/ipol.2017.179","article-title":"The Bilateral Filter for Point Clouds","volume":"7","author":"Digne","year":"2017","journal-title":"Image Process. On Line"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.image.2017.05.009","article-title":"A review of algorithms for filtering the 3D point cloud","volume":"57","author":"Han","year":"2017","journal-title":"Signal Process. Image Commun."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"129029","DOI":"10.1109\/ACCESS.2019.2939684","article-title":"A Novel Simplification Method for 3D Geometric Point Cloud Based on the Importance of Point","volume":"7","author":"Ji","year":"2019","journal-title":"IEEE Access"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.isprsjprs.2015.01.016","article-title":"Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers","volume":"105","author":"Weinmann","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.1080\/17538947.2018.1503740","article-title":"A Hierarchical unsupervised method for power line classification from airborne LiDAR data","volume":"12","author":"Wang","year":"2019","journal-title":"Int. J. Digit. Earth"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"43","DOI":"10.5194\/isprs-annals-IV-2-W4-43-2017","article-title":"Using multiscale features for the 3D semantic labeling of airborne laser scanning data","volume":"4","author":"Blomley","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.isprsjprs.2017.02.012","article-title":"Analytical and numerical investigations on the accuracy and robustness of geometric features extracted from 3D point cloud data","volume":"126","author":"Dittrich","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","first-page":"1","article-title":"Adaptive Multiscale Feature Extraction in a Distributed System for Semantic Classification of Airborne LiDAR Point Clouds","volume":"19","author":"Singh","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Che, E., Jung, J., and Olsen, M.J. (2019). Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review. Sensors, 19.","DOI":"10.3390\/s19040810"},{"key":"ref_43","first-page":"333","article-title":"Semantic classification of sandstone landscape point cloud based on neighbourhood features","volume":"43","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"103656","DOI":"10.1016\/j.tust.2020.103656","article-title":"A machine learning approach for the detection of supporting rock bolts from laser scan data in an underground mine","volume":"107","author":"Gallwey","year":"2021","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ni, H., Lin, X., and Zhang, J. (2017). Classification of ALS Point Cloud with Improved Point Cloud Segmentation and Random Forests. Remote Sens., 9.","DOI":"10.3390\/rs9030288"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.isprsjprs.2017.03.001","article-title":"Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning","volume":"140","author":"Vetrivel","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Thomas, H., Goulette, F., Deschaud, J.E., Marcotegui, B., and LeGall, Y. (2018, January 5\u20138). Semantic classification of 3D point clouds with multiscale spherical neighborhoods. Proceedings of the 2018 International Conference on 3D vision (3DV), Verona, Italy.","DOI":"10.1109\/3DV.2018.00052"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.isprsjprs.2018.12.009","article-title":"A random forest classifier based on pixel comparison features for urban LiDAR data","volume":"148","author":"Wang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","first-page":"441","article-title":"Classification of LiDAR data with point based classification methods","volume":"XLI-B3","author":"Yastikli","year":"2016","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lucas, C., Bouten, W., Koma, Z., Kissling, W.D., and Seijmonsbergen, A.C. (2019). Identification of Linear Vegetation Elements in a Rural Landscape Using LiDAR Point Clouds. Remote Sens., 11.","DOI":"10.3390\/rs11030292"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.isprsjprs.2012.01.006","article-title":"3D terrestrial lidar data classification of complex natural scenes using a multiscale dimensionality criterion: Applications in geomorphology","volume":"68","author":"Brodu","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","first-page":"99","article-title":"Selection of LiDAR geometric features with adaptive neighborhood size for urban land cover classification","volume":"60","author":"Dong","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_53","first-page":"W12","article-title":"Dimensionality based scale selection in 3D LiDAR point clouds","volume":"38","author":"Demantke","year":"2011","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1109\/LGRS.2019.2927779","article-title":"Multi-Scale Local Context Embedding for LiDAR Point Cloud Classification","volume":"17","author":"Huang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.ijmst.2021.01.001","article-title":"A robust approach to identify roof bolts in 3D point cloud data captured from a mobile laser scanner","volume":"31","author":"Singh","year":"2021","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_57","unstructured":"Blanco, J.L., and Rai, P.K. (2014, May 02). Nanoflann: A C++ header-only fork of FLANN, a library for Nearest Neighbor (NN) with KD-trees. Available online: https:\/\/github.com\/jlblancoc\/nanoflann."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v077.i01","article-title":"Ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R","volume":"77","author":"Wright","year":"2017","journal-title":"J. Stat. Softw."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"109","DOI":"10.5194\/isprs-annals-IV-1-109-2018","article-title":"Feasibility investigation on single photon LiDAR based water surface mapping","volume":"IV-1","author":"Mandlburger","year":"2018","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_60","unstructured":"Government, N. (2019, March 19). Navarra Dataset. Available online: https:\/\/filescartografia.navarra.es."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Hu, Q., Yang, B., Xie, L., Rosa, S., Guo, Y., Wang, Z., Trigoni, N., and Markham, A. (2020, January 13\u201319). RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01112"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/269\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T17:56:20Z","timestamp":1760118980000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/269"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,2]]},"references-count":61,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15010269"],"URL":"https:\/\/doi.org\/10.3390\/rs15010269","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,1,2]]}}}