{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T10:34:40Z","timestamp":1774434880233,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T00:00:00Z","timestamp":1635292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China under Grant","award":["61701123, 61805048, 51905351"],"award-info":[{"award-number":["61701123, 61805048, 51905351"]}]},{"name":"Guangdong Provincial Key Laboratory of Cyber-Physical System under Grant","award":["2020B1212060069"],"award-info":[{"award-number":["2020B1212060069"]}]},{"name":"High Resolution Earth Observation Major Project under Grant","award":["83-Y40G33-9001-18\/20"],"award-info":[{"award-number":["83-Y40G33-9001-18\/20"]}]},{"name":"Provincial Agricultural Science and Technology Innovation and Extension project of Guangdong Province under Grant","award":["2019KJ147"],"award-info":[{"award-number":["2019KJ147"]}]},{"name":"Opening Foundation of Key Laboratory of Environment Change and Resources Use in Beibu Gulf Ministry of Education (Nanning Normal University)","award":["NNNU-KLOP-K1935, NNNU-KLOPK1936"],"award-info":[{"award-number":["NNNU-KLOP-K1935, NNNU-KLOPK1936"]}]},{"DOI":"10.13039\/501100012245","name":"Science and technology projects of Guangdong Province","doi-asserted-by":"publisher","award":["2016B010127005"],"award-info":[{"award-number":["2016B010127005"]}],"id":[{"id":"10.13039\/501100012245","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Planning Project of Shenzhen Municipality of China under Grant","award":["JCYJ20190808113413430"],"award-info":[{"award-number":["JCYJ20190808113413430"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Point cloud classification plays a significant role in Light Detection and Ranging (LiDAR) applications. However, most available multi-scale feature learning networks for large-scale 3D LiDAR point cloud classification tasks are time-consuming. In this paper, an efficient deep neural architecture denoted as Point Expanded Multi-scale Convolutional Network (PEMCNet) is developed to accurately classify the 3D LiDAR point cloud. Different from traditional networks for point cloud processing, PEMCNet includes successive Point Expanded Grouping (PEG) units and Absolute and Relative Spatial Embedding (ARSE) units for representative point feature learning. The PEG unit enables us to progressively increase the receptive field for each observed point and aggregate the feature of a point cloud at different scales but without increasing computation. The ARSE unit following the PEG unit furthermore realizes representative encoding of points relationship, which effectively preserves the geometric details between points. We evaluate our method on both public datasets (the Urban Semantic 3D (US3D) dataset and Semantic3D benchmark dataset) and our new collected Unmanned Aerial Vehicle (UAV) based LiDAR point cloud data of the campus of Guangdong University of Technology. In comparison with four available state-of-the-art methods, our methods ranked first place regarding both efficiency and accuracy. It was observed on the public datasets that with a 2% increase in classification accuracy, over 26% improvement of efficiency was achieved at the same time compared to the second efficient method. Its potential value is also tested on the newly collected point cloud data with over 91% of classification accuracy and 154 ms of processing time.<\/jats:p>","DOI":"10.3390\/rs13214312","type":"journal-article","created":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T23:24:42Z","timestamp":1635377082000},"page":"4312","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["PEMCNet: An Efficient Multi-Scale Point Feature Fusion Network for 3D LiDAR Point Cloud Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3360-1756","authenticated-orcid":false,"given":"Genping","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Computers, Guangdong University and Technology, Guangzhou 510006, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9203-6227","authenticated-orcid":false,"given":"Weiguang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computers, Guangdong University and Technology, Guangzhou 510006, China"}]},{"given":"Yeping","family":"Peng","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China"}]},{"given":"Heng","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Automation, Guangdong University of Technology, Guangzhou 510006, China"}]},{"given":"Zhuowei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computers, Guangdong University and Technology, Guangzhou 510006, China"},{"name":"School of Computer Science, Wuhan Donghu University, Wuhan 430074, China"}]},{"given":"Lianglun","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computers, Guangdong University and Technology, Guangzhou 510006, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.isprsjprs.2020.02.004","article-title":"Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification","volume":"162","author":"Wen","year":"2020","journal-title":"ISPRS J. 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