{"id":"https://openalex.org/W2985792721","doi":"https://doi.org/10.1109/igarss.2019.8899163","title":"Improved Deep Fully Convolutional Network with Superpixel-Based Conditional Random Fields for Building Extraction","display_name":"Improved Deep Fully Convolutional Network with Superpixel-Based Conditional Random Fields for Building Extraction","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2985792721","doi":"https://doi.org/10.1109/igarss.2019.8899163","mag":"2985792721"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8899163","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8899163","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","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/A5036964506","display_name":"Wenqing Feng","orcid":"https://orcid.org/0000-0001-7763-5870"},"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"]},{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenqing Feng","raw_affiliation_strings":["State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, P.R. China","institution_ids":["https://openalex.org/I37461747","https://openalex.org/I4210118728"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052272301","display_name":"Haigang Sui","orcid":"https://orcid.org/0000-0003-4405-3553"},"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"]},{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haigang Sui","raw_affiliation_strings":["State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, P.R. China","institution_ids":["https://openalex.org/I37461747","https://openalex.org/I4210118728"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100726322","display_name":"Hua Li","orcid":"https://orcid.org/0000-0002-0745-035X"},"institutions":[{"id":"https://openalex.org/I204823248","display_name":"Huazhong Agricultural University","ror":"https://ror.org/023b72294","country_code":"CN","type":"education","lineage":["https://openalex.org/I204823248"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Hua","raw_affiliation_strings":["College of Resources and Environment, Huazhong Agricultural University, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Resources and Environment, Huazhong Agricultural University, P.R. China","institution_ids":["https://openalex.org/I204823248"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024713117","display_name":"Chuan Xu","orcid":"https://orcid.org/0000-0002-7099-7833"},"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"]},{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuan Xu","raw_affiliation_strings":["State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, P.R. China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, P.R. China","institution_ids":["https://openalex.org/I37461747","https://openalex.org/I4210118728"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"52","last_page":"55"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9969000220298767,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9966999888420105,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.9336949586868286},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7255639433860779},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5339535474777222},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5093344449996948},{"id":"https://openalex.org/keywords/extraction","display_name":"Extraction (chemistry)","score":0.498382568359375},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3907995820045471}],"concepts":[{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.9336949586868286},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7255639433860779},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5339535474777222},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5093344449996948},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.498382568359375},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3907995820045471},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8899163","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8899163","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W6908809","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2118246710","https://openalex.org/W2395611524","https://openalex.org/W2412782625","https://openalex.org/W2538244214","https://openalex.org/W2609402060","https://openalex.org/W2794948653","https://openalex.org/W2904122576","https://openalex.org/W2963525222","https://openalex.org/W2963881378","https://openalex.org/W2971095420","https://openalex.org/W4302799888","https://openalex.org/W6600284362","https://openalex.org/W6639824700","https://openalex.org/W6640054144","https://openalex.org/W6744399899","https://openalex.org/W6757096245"],"related_works":["https://openalex.org/W2356597680","https://openalex.org/W2093471820","https://openalex.org/W50079190","https://openalex.org/W2114846443","https://openalex.org/W3102147106","https://openalex.org/W2347460059","https://openalex.org/W2111726165","https://openalex.org/W3136048405","https://openalex.org/W2033914206","https://openalex.org/W2042327336"],"abstract_inverted_index":{"Fully":[0],"convolutional":[1,46],"network":[2,49],"(FCN)":[3],"modeling":[4],"is":[5,11,20,75,112],"a":[6],"recently":[7],"developed":[8],"technique":[9],"that":[10,50,125],"capable":[12],"of":[13,24,60,84,100,117,145],"significantly":[14],"enhancing":[15],"building":[16,36,55,85],"extraction":[17,56,83],"accuracy;":[18],"it":[19],"an":[21,43,110],"important":[22],"branch":[23],"deep":[25,45],"learning":[26],"and":[27,90,104,132,140],"uses":[28],"advanced":[29],"state-of-the-art":[30,150],"techniques,":[31],"especially":[32],"with":[33,70,148],"regard":[34],"to":[35,77,105,114],"segmentation.":[37],"In":[38],"this":[39],"paper,":[40],"we":[41],"present":[42],"enhanced":[44],"encoder-decoder":[47],"(DCED)":[48],"has":[51],"been":[52],"customized":[53],"for":[54,81],"through":[57],"the":[58,79,98,107,115,118,126,137,142,156],"application":[59],"superpixel-based":[61],"conditional":[62],"random":[63],"fields":[64],"(SCRFs).":[65],"The":[66],"improved":[67,119],"DCED":[68,120],"network,":[69],"symmetrical":[71],"dense-shortcut":[72],"connection":[73],"structures,":[74],"employed":[76],"establish":[78],"encoders":[80,89],"automatic":[82],"features.":[86],"Our":[87],"network's":[88],"decoders":[91],"are":[92],"also":[93],"symmetrical.":[94],"To":[95],"further":[96],"reduce":[97],"occurrence":[99],"falsely":[101],"segmented":[102],"buildings,":[103],"sharpen":[106],"buildings'":[108],"boundaries,":[109],"SCRF":[111],"added":[113],"end":[116],"architecture.":[121],"Experimental":[122],"results":[123],"indicate":[124],"proposed":[127],"approach":[128],"exhibits":[129],"competitive":[130],"quantitative":[131],"qualitative":[133],"performance,":[134],"effectively":[135],"alleviating":[136],"salt-and-pepper":[138],"phenomenon":[139],"retaining":[141],"edge":[143],"structures":[144],"buildings.":[146],"Compared":[147],"other":[149],"methods,":[151],"our":[152],"method":[153],"demonstrably":[154],"achieves":[155],"optimal":[157],"final":[158],"accuracies.":[159]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}