{"id":"https://openalex.org/W3194343332","doi":"https://doi.org/10.1109/lgrs.2021.3102599","title":"DGCNN Network Architecture With Densely Connected Point Pairs in Multiscale Local Regions for ALS Point Cloud Classification","display_name":"DGCNN Network Architecture With Densely Connected Point Pairs in Multiscale Local Regions for ALS Point Cloud Classification","publication_year":2021,"publication_date":"2021-08-20","ids":{"openalex":"https://openalex.org/W3194343332","doi":"https://doi.org/10.1109/lgrs.2021.3102599","mag":"3194343332"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2021.3102599","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2021.3102599","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090395787","display_name":"Yang Chen","orcid":"https://orcid.org/0000-0001-5874-9944"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Chen","raw_affiliation_strings":["School of Geodesy and Geomatics, Wuhan University, Wuhan, China","[School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China (e-mail: ymxu@sgg.whu.edu.cn)]"],"raw_orcid":"https://orcid.org/0000-0001-5874-9944","affiliations":[{"raw_affiliation_string":"School of Geodesy and Geomatics, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]},{"raw_affiliation_string":"[School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China (e-mail: ymxu@sgg.whu.edu.cn)]","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101656229","display_name":"Yaming Xu","orcid":"https://orcid.org/0000-0001-6422-0684"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaming Xu","raw_affiliation_strings":["School of Geodesy and Geomatics, Wuhan University, Wuhan, China","School of Geodesy and Geomatics, Wuhan University, Wuhan, 430079, China"],"raw_orcid":"https://orcid.org/0000-0001-6422-0684","affiliations":[{"raw_affiliation_string":"School of Geodesy and Geomatics, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]},{"raw_affiliation_string":"School of Geodesy and Geomatics, Wuhan University, Wuhan, 430079, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090859671","display_name":"Yin Xing","orcid":"https://orcid.org/0000-0002-3328-0569"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yin Xing","raw_affiliation_strings":["School of Earth Sciences and Engineering, Hohai University, Nanjing, China","School of Earth Sciences and Engineering , Hohai University, Nanjing 211100, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Earth Sciences and Engineering, Hohai University, Nanjing, China","institution_ids":["https://openalex.org/I163340411"]},{"raw_affiliation_string":"School of Earth Sciences and Engineering , Hohai University, Nanjing 211100, China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067061325","display_name":"Guanlan Liu","orcid":"https://orcid.org/0000-0001-5140-8493"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanlan Liu","raw_affiliation_strings":["School of Geodesy and Geomatics, Wuhan University, Wuhan, China","School of Geodesy and Geomatics, Wuhan University, Wuhan, 430079, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Geodesy and Geomatics, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]},{"raw_affiliation_string":"School of Geodesy and Geomatics, Wuhan University, Wuhan, 430079, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5494,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.62191704,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11211","display_name":"3D Surveying and Cultural Heritage","score":0.9965999722480774,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9736999869346619,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.8383603096008301},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6743006110191345},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.5442091226577759},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5084051489830017},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49723413586616516},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.43782421946525574},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4361238479614258},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.43008798360824585},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.416240394115448},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3408215045928955},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.24304190278053284}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.8383603096008301},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6743006110191345},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.5442091226577759},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5084051489830017},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49723413586616516},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.43782421946525574},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4361238479614258},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.43008798360824585},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.416240394115448},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3408215045928955},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.24304190278053284},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2021.3102599","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2021.3102599","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4586687812","display_name":null,"funder_award_id":"41904170","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2040861824","https://openalex.org/W2088405070","https://openalex.org/W2278868814","https://openalex.org/W2513955405","https://openalex.org/W2915801748","https://openalex.org/W2953785039","https://openalex.org/W2965945478","https://openalex.org/W2969121582","https://openalex.org/W2982639230","https://openalex.org/W2988892772","https://openalex.org/W3008274274","https://openalex.org/W3016245793","https://openalex.org/W4212905584","https://openalex.org/W6739778489","https://openalex.org/W6752110883","https://openalex.org/W6753266022","https://openalex.org/W6763422710"],"related_works":["https://openalex.org/W2356597680","https://openalex.org/W2114846443","https://openalex.org/W3102147106","https://openalex.org/W2093471820","https://openalex.org/W2347460059","https://openalex.org/W50079190","https://openalex.org/W2111726165","https://openalex.org/W3136048405","https://openalex.org/W3137866197","https://openalex.org/W2741749319"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"in":[2,29,85,112],"3-D":[3,156],"classification":[4],"tasks":[5],"focuses":[6],"on":[7,145],"the":[8,26,30,34,41,50,58,68,106,122,126,129,137,146,149],"designs":[9],"of":[10,61,108,125,148],"comprehensive":[11],"local":[12,87],"aggregation":[13],"operators.":[14],"Airborne":[15],"laser":[16],"scanning":[17],"(ALS)":[18],"point":[19,63,83,138,168],"clouds":[20],"have":[21],"their":[22],"own":[23],"characteristics:":[24],"1)":[25],"object":[27],"overlaps":[28],"vertical":[31],"direction;":[32],"2)":[33],"spatial":[35],"density":[36],"is":[37,102,133],"uneven;":[38],"and":[39,71,161],"3)":[40],"objects":[42],"present":[43],"large-scale":[44],"variations":[45],"between":[46],"different":[47],"categories.":[48],"However,":[49],"dynamic":[51],"graph":[52],"convolutional":[53],"neural":[54,75],"network":[55,70,76],"(DGCNN)":[56],"ignores":[57,121],"inherent":[59],"properties":[60],"ALS":[62,113,167],"clouds.":[64],"This":[65],"study":[66],"modifies":[67],"DGCNN":[69],"proposes":[72],"a":[73],"new":[74],"module":[77],"called":[78],"Webconv,":[79],"which":[80],"densely":[81],"connects":[82],"pairs":[84],"multiscale":[86],"regions":[88],"to":[89,104,135],"learn":[90],"contextual":[91],"information.":[92],"One":[93],"modified":[94],"cross":[95],"entropy":[96],"loss":[97],"function":[98],"with":[99],"variable":[100],"weight":[101],"proposed":[103],"solve":[105],"problem":[107],"uneven":[109],"category":[110],"distributions":[111],"points.":[114],"Because":[115],"preprocessing":[116],"such":[117],"as":[118],"block":[119],"partition":[120],"context":[123],"information":[124],"whole":[127],"region,":[128],"conditional":[130],"random":[131],"field":[132],"used":[134,165],"refine":[136],"cloud.":[139],"Our":[140],"approach":[141],"achieves":[142],"state-of-the-art":[143],"performance":[144],"dataset":[147],"2019":[150],"IEEE":[151],"GRSS":[152],"Data":[153],"Fusion":[154],"Contest":[155],"Point":[157],"Cloud":[158],"Classification":[159],"Challenge":[160],"can":[162],"be":[163],"widely":[164],"for":[166],"classification.":[169]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
