{"id":"https://openalex.org/W4285167737","doi":"https://doi.org/10.1109/lgrs.2022.3183613","title":"A Multisensor Data Fusion Model for Semantic Segmentation in Aerial Images","display_name":"A Multisensor Data Fusion Model for Semantic Segmentation in Aerial Images","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4285167737","doi":"https://doi.org/10.1109/lgrs.2022.3183613"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2022.3183613","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2022.3183613","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/A5024029157","display_name":"Qian Weng","orcid":"https://orcid.org/0000-0002-5307-4770"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qian Weng","raw_affiliation_strings":["College of Computer and Data Science, Fuzhou University, Fuzhou, China","Fujian Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer and Data Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]},{"raw_affiliation_string":"Fujian Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051449817","display_name":"Hao Chen","orcid":"https://orcid.org/0000-0003-4935-1031"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Chen","raw_affiliation_strings":["College of Computer and Data Science, Fuzhou University, Fuzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer and Data Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100605061","display_name":"Hongli Chen","orcid":"https://orcid.org/0000-0002-9002-2603"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongli Chen","raw_affiliation_strings":["College of Computer and Data Science, Fuzhou University, Fuzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer and Data Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100643723","display_name":"Wenzhong Guo","orcid":"https://orcid.org/0000-0003-4118-8823"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenzhong Guo","raw_affiliation_strings":["College of Computer and Data Science, Fuzhou University, Fuzhou, China"],"affiliations":[{"raw_affiliation_string":"College of Computer and Data Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048637476","display_name":"Zhengyuan Mao","orcid":"https://orcid.org/0000-0002-4388-5270"},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengyuan Mao","raw_affiliation_strings":["Academy of Digital China, Fuzhou University, Fuzhou, China"],"affiliations":[{"raw_affiliation_string":"Academy of Digital China, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5024029157"],"corresponding_institution_ids":["https://openalex.org/I80947539"],"apc_list":null,"apc_paid":null,"fwci":1.0062,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.76612001,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987999796867371,"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":0.9972000122070312,"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/upsampling","display_name":"Upsampling","score":0.8929892778396606},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8033155202865601},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7627295255661011},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6908056139945984},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5297698974609375},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.48064982891082764},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.47325852513313293},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4694228768348694},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4662432074546814},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4432101845741272},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.43592917919158936},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.15903133153915405}],"concepts":[{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.8929892778396606},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8033155202865601},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7627295255661011},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6908056139945984},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5297698974609375},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.48064982891082764},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.47325852513313293},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4694228768348694},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4662432074546814},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4432101845741272},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.43592917919158936},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.15903133153915405}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2022.3183613","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2022.3183613","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/G2090562274","display_name":null,"funder_award_id":"41801324","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2634340972","display_name":null,"funder_award_id":"2020J05114","funder_id":"https://openalex.org/F4320321878","funder_display_name":"Natural Science Foundation of Fujian Province"},{"id":"https://openalex.org/G7128780185","display_name":null,"funder_award_id":"2019J01244","funder_id":"https://openalex.org/F4320321878","funder_display_name":"Natural Science Foundation of Fujian Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321878","display_name":"Natural Science Foundation of Fujian Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1903029394","https://openalex.org/W2194775991","https://openalex.org/W2412782625","https://openalex.org/W2480078828","https://openalex.org/W2551751523","https://openalex.org/W2618943282","https://openalex.org/W2921781974","https://openalex.org/W2963378109","https://openalex.org/W2963881378","https://openalex.org/W2963995737","https://openalex.org/W2965719507","https://openalex.org/W2995766874","https://openalex.org/W3009297390","https://openalex.org/W3018169007","https://openalex.org/W3102850314","https://openalex.org/W3200556990"],"related_works":["https://openalex.org/W2062399876","https://openalex.org/W2607795551","https://openalex.org/W3155117723","https://openalex.org/W1991429770","https://openalex.org/W1983892167","https://openalex.org/W2281134365","https://openalex.org/W4310746709","https://openalex.org/W4306309518","https://openalex.org/W4385574037","https://openalex.org/W4212888438"],"abstract_inverted_index":{"Semantic":[0],"segmentation":[1,20,132],"in":[2,94],"high-resolution":[3,43],"aerial":[4],"images":[5],"is":[6,31,92],"a":[7,13,59,79],"fundamental":[8],"and":[9,119],"challenging":[10],"task":[11],"with":[12,22,37],"wide":[14],"range":[15],"of":[16,99,109,111],"applications.":[17],"Although":[18],"many":[19],"methods":[21,50,140],"convolutional":[23],"neural":[24],"networks":[25],"have":[26],"achieved":[27],"inspiring":[28],"results,":[29],"it":[30],"still":[32],"difficult":[33],"to":[34,53,104],"distinguish":[35],"regions":[36],"similar":[38],"spectral":[39],"features":[40],"only":[41],"using":[42],"data.":[44],"Besides,":[45],"the":[46,66,95,100,106,112,135,142],"traditional":[47],"data-independent":[48],"upsampling":[49,89],"may":[51],"lead":[52],"sub-optimal":[54],"results.":[55],"This":[56],"letter":[57],"proposes":[58],"multisensor":[60],"data":[61,77],"fusion":[62],"model":[63],"(MSDFM).":[64],"Following":[65],"classical":[67],"encoder-decoder":[68],"structure,":[69],"MSDFM":[70,126],"regards":[71],"colored":[72],"digital":[73],"surface":[74],"models":[75],"(colored-DSM)":[76],"as":[78],"complementary":[80],"input":[81],"for":[82,134],"further":[83],"detailed":[84],"feature":[85],"extraction.":[86],"A":[87],"data-dependent":[88],"(DUpsampling)":[90],"method":[91],"adopted":[93],"decoder":[96],"stage":[97],"instead":[98],"common":[101],"up-sampling":[102],"approaches":[103],"improve":[105],"classification":[107],"accuracy":[108],"pixels":[110],"small":[113],"objects.":[114],"Extensive":[115],"experiments":[116],"on":[117],"Vaihingen":[118,144],"Potsdam":[120],"datasets":[121],"demonstrate":[122],"that":[123],"our":[124],"proposed":[125],"outperforms":[127],"most":[128],"related":[129],"models.":[130],"Significantly,":[131],"performance":[133],"car":[136],"category":[137],"surpasses":[138],"state-of-the-art":[139],"over":[141],"ISPRS":[143],"dataset.":[145]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
