{"id":"https://openalex.org/W4382053036","doi":"https://doi.org/10.1109/icccs57501.2023.10150515","title":"A Connected Sets Detection Morphological Filter for Airborne LiDAR DTM Extraction under Urban Area","display_name":"A Connected Sets Detection Morphological Filter for Airborne LiDAR DTM Extraction under Urban Area","publication_year":2023,"publication_date":"2023-04-21","ids":{"openalex":"https://openalex.org/W4382053036","doi":"https://doi.org/10.1109/icccs57501.2023.10150515"},"language":"en","primary_location":{"id":"doi:10.1109/icccs57501.2023.10150515","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccs57501.2023.10150515","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 8th International Conference on Computer and Communication Systems (ICCCS)","raw_type":"proceedings-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/A5101505682","display_name":"Wenhao Wu","orcid":"https://orcid.org/0000-0002-4988-8704"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenhao Wu","raw_affiliation_strings":["School of Mathematics and Statistics, xsXidian University,Xi&#x0027;an,China"],"affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, xsXidian University,Xi&#x0027;an,China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5101505682"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.1256,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.41207327,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"469","last_page":"474"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":1.0,"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":1.0,"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":0.9983999729156494,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9879999756813049,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.786213219165802},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6978130340576172},{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.6286897659301758},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.5972411036491394},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5791031122207642},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.572503387928009},{"id":"https://openalex.org/keywords/terrain","display_name":"Terrain","score":0.5524083375930786},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5378292202949524},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.5123251676559448},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5077765583992004},{"id":"https://openalex.org/keywords/grayscale","display_name":"Grayscale","score":0.4954780340194702},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.47900813817977905},{"id":"https://openalex.org/keywords/digital-elevation-model","display_name":"Digital elevation model","score":0.4313991367816925},{"id":"https://openalex.org/keywords/mathematical-morphology","display_name":"Mathematical morphology","score":0.4198110103607178},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.332099050283432},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.3093056082725525},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.22217246890068054},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.15880253911018372}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.786213219165802},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6978130340576172},{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.6286897659301758},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.5972411036491394},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5791031122207642},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.572503387928009},{"id":"https://openalex.org/C161840515","wikidata":"https://www.wikidata.org/wiki/Q186131","display_name":"Terrain","level":2,"score":0.5524083375930786},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5378292202949524},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.5123251676559448},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5077765583992004},{"id":"https://openalex.org/C78201319","wikidata":"https://www.wikidata.org/wiki/Q685727","display_name":"Grayscale","level":3,"score":0.4954780340194702},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.47900813817977905},{"id":"https://openalex.org/C181843262","wikidata":"https://www.wikidata.org/wiki/Q640492","display_name":"Digital elevation model","level":2,"score":0.4313991367816925},{"id":"https://openalex.org/C185568154","wikidata":"https://www.wikidata.org/wiki/Q530242","display_name":"Mathematical morphology","level":4,"score":0.4198110103607178},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.332099050283432},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.3093056082725525},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.22217246890068054},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.15880253911018372},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icccs57501.2023.10150515","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icccs57501.2023.10150515","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 8th International Conference on Computer and Communication Systems (ICCCS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.8399999737739563}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W48960425","https://openalex.org/W1494100285","https://openalex.org/W1974696470","https://openalex.org/W1995807936","https://openalex.org/W2003099669","https://openalex.org/W2027781877","https://openalex.org/W2034546021","https://openalex.org/W2065438578","https://openalex.org/W2075513266","https://openalex.org/W2149059790","https://openalex.org/W2163973654","https://openalex.org/W2168232342","https://openalex.org/W2973135792","https://openalex.org/W3106645491","https://openalex.org/W3177354419","https://openalex.org/W4312538217","https://openalex.org/W6684231901"],"related_works":["https://openalex.org/W138221400","https://openalex.org/W4317671434","https://openalex.org/W2922872563","https://openalex.org/W2549418288","https://openalex.org/W2739092184","https://openalex.org/W2740804836","https://openalex.org/W4321317645","https://openalex.org/W1586076277","https://openalex.org/W2032529054","https://openalex.org/W433234986"],"abstract_inverted_index":{"In":[0,27,53,105,118,156],"recent":[1],"years,":[2],"Point":[3],"Cloud":[4],"signal":[5],"processing":[6],"has":[7],"received":[8],"increased":[9],"attention.":[10],"Airborne":[11],"LiDAR":[12],"can":[13],"measure":[14],"the":[15,73,88,92,101,109,114,124,129,138,141,150,153,191],"ground":[16],"and":[17,24,43,80,111,135],"generate":[18,30],"point":[19,38,50,74,125,166],"clouds":[20],"in":[21],"a":[22,77,181],"cost-effective":[23],"rapid":[25],"way.":[26],"order":[28,106],"to":[29,107,147,158,173,180],"an":[31,58],"accurate":[32],"Digital":[33],"Terrain":[34],"Model":[35],"(DTM),":[36],"non-ground":[37,82],"such":[39],"as":[40],"buildings,":[41],"vehicles,":[42],"vegetation":[44],"must":[45],"be":[46,200],"removed,":[47],"that":[48],"is,":[49],"cloud":[51,75,126,167],"filtering.":[52],"this":[54,119],"paper,":[55,120],"we":[56,121,160],"propose":[57,162],"adaptive":[59,132],"morphological":[60,69,85,115],"filtering":[61,70,116,154],"algorithm":[62,71,146],"designed":[63],"specifically":[64],"for":[65,165],"urban":[66],"area.":[67],"The":[68,184],"transforms":[72],"into":[76],"grayscale":[78],"image":[79,131],"identifies":[81],"locations":[83],"using":[84,128,140],"operations.":[86],"However,":[87],"method":[89],"requires":[90],"setting":[91],"filter":[93],"window":[94],"size":[95,151],"manually.":[96],"Improper":[97],"parameters":[98],"will":[99,199],"affect":[100],"accuracy":[102],"of":[103,113,152,193],"DTM.":[104],"improve":[108],"adaptivity":[110],"robustness":[112],"algorithm.":[117],"first":[122],"segment":[123],"images":[127],"integral":[130],"segmentation":[133],"algorithm,":[134],"then":[136],"detect":[137],"buildings":[139],"proposed":[142,195],"connected":[143],"sets":[144],"detection":[145],"automatically":[148],"determine":[149],"window.":[155],"addition":[157],"this,":[159],"also":[161],"dynamic":[163],"thresholding":[164],"filtering,":[168],"which":[169],"performs":[170],"better":[171],"compared":[172],"previous":[174],"methods":[175],"where":[176],"it":[177],"is":[178],"set":[179],"constant":[182],"threshold.":[183],"experimental":[185],"results":[186],"on":[187],"15":[188],"samples":[189],"demonstrate":[190],"effectiveness":[192],"our":[194],"method.":[196],"Our":[197],"code":[198],"available":[201],"at":[202],"https://github.com/xdu-whh/ICCCS2023-point-cloud-filtering.":[203]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
