{"id":"https://openalex.org/W4361769328","doi":"https://doi.org/10.1109/lgrs.2023.3262586","title":"Integrating Spatial Details With Long-Range Contexts for Semantic Segmentation of Very High-Resolution Remote-Sensing Images","display_name":"Integrating Spatial Details With Long-Range Contexts for Semantic Segmentation of Very High-Resolution Remote-Sensing Images","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4361769328","doi":"https://doi.org/10.1109/lgrs.2023.3262586"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2023.3262586","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2023.3262586","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/A5101521809","display_name":"Long Jiang","orcid":"https://orcid.org/0000-0003-1969-0980"},"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":"Jiang Long","raw_affiliation_strings":["Key Laboratory of Spatial Data Mining and Information Sharing of the Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-1969-0980","affiliations":[{"raw_affiliation_string":"Key Laboratory of Spatial Data Mining and Information Sharing of the Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100348210","display_name":"Mengmeng Li","orcid":"https://orcid.org/0000-0002-9083-0475"},"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":"Mengmeng Li","raw_affiliation_strings":["Key Laboratory of Spatial Data Mining and Information Sharing of the Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-9083-0475","affiliations":[{"raw_affiliation_string":"Key Laboratory of Spatial Data Mining and Information Sharing of the Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038733818","display_name":"Xiaoqin Wang","orcid":"https://orcid.org/0000-0002-9619-1918"},"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":"Xiaoqin Wang","raw_affiliation_strings":["Key Laboratory of Spatial Data Mining and Information Sharing of the Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-9619-1918","affiliations":[{"raw_affiliation_string":"Key Laboratory of Spatial Data Mining and Information Sharing of the Ministry of Education, Academy of Digital China (Fujian), Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I80947539"],"apc_list":null,"apc_paid":null,"fwci":3.2536,"has_fulltext":false,"cited_by_count":34,"citation_normalized_percentile":{"value":0.9360438,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"20","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9851999878883362,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9851999878883362,"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":0.970300018787384,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9567000269889832,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8127901554107666},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7450883388519287},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7248417139053345},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.724644660949707},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6570429801940918},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5673247575759888},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5531694293022156},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.528360903263092},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4934655725955963},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.45862171053886414},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.4469451606273651},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.37780383229255676},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.32022625207901}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8127901554107666},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7450883388519287},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7248417139053345},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.724644660949707},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6570429801940918},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5673247575759888},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5531694293022156},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.528360903263092},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4934655725955963},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.45862171053886414},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.4469451606273651},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.37780383229255676},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.32022625207901},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2023.3262586","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2023.3262586","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/G1028603937","display_name":null,"funder_award_id":"2022N0019","funder_id":"https://openalex.org/F4320317314","funder_display_name":"Science and Technology Projects of Fujian Province"},{"id":"https://openalex.org/G4984427045","display_name":null,"funder_award_id":"42001283","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7047474693","display_name":null,"funder_award_id":"2022C0024","funder_id":"https://openalex.org/F4320317314","funder_display_name":"Science and Technology Projects of Fujian Province"}],"funders":[{"id":"https://openalex.org/F4320317314","display_name":"Science and Technology Projects of Fujian Province","ror":null},{"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":35,"referenced_works":["https://openalex.org/W2094682449","https://openalex.org/W2774320778","https://openalex.org/W2908320224","https://openalex.org/W2962812282","https://openalex.org/W2963794428","https://openalex.org/W2982206001","https://openalex.org/W2991441757","https://openalex.org/W3003394660","https://openalex.org/W3104035745","https://openalex.org/W3112929693","https://openalex.org/W3138516171","https://openalex.org/W3168972675","https://openalex.org/W3171427178","https://openalex.org/W3204166336","https://openalex.org/W3205865791","https://openalex.org/W3211329537","https://openalex.org/W3217005392","https://openalex.org/W4205138939","https://openalex.org/W4206007731","https://openalex.org/W4213172700","https://openalex.org/W4224979365","https://openalex.org/W4226224140","https://openalex.org/W4226289601","https://openalex.org/W4282934494","https://openalex.org/W4285204591","https://openalex.org/W4285262453","https://openalex.org/W4285290151","https://openalex.org/W4307974001","https://openalex.org/W4312349930","https://openalex.org/W4312603774","https://openalex.org/W4312611768","https://openalex.org/W4385245566","https://openalex.org/W6750469568","https://openalex.org/W6762718338","https://openalex.org/W6846529796"],"related_works":["https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W4205302943","https://openalex.org/W2119949815","https://openalex.org/W2142795561","https://openalex.org/W3155418658","https://openalex.org/W2379948177","https://openalex.org/W2107949441","https://openalex.org/W2561132942","https://openalex.org/W2334580170"],"abstract_inverted_index":{"This":[0],"letter":[1],"presents":[2],"a":[3,47,70,119,183],"cross-learning":[4],"network":[5],"(i.e.,":[6],"CLCFormer)":[7],"integrating":[8],"fine-grained":[9],"spatial":[10,107],"details":[11],"within":[12],"long-range":[13],"global":[14],"contexts":[15],"based":[16],"upon":[17],"convolutional":[18],"neural":[19],"networks":[20],"(CNNs)":[21],"and":[22,44,46,56,95,143,146,150,173,186],"transformer,":[23,45],"for":[24,90,127,189],"semantic":[25,62,191],"segmentation":[26,88,133,192],"of":[27,113,193,198],"very":[28],"high-resolution":[29],"(VHR)":[30],"remote-sensing":[31],"images.":[32,195],"More":[33],"specifically,":[34],"CLCFormer":[35,181],"comprises":[36],"two":[37],"parallel":[38],"encoders,":[39],"derived":[40],"from":[41,58],"the":[42,60,140,162,190,199],"CNN":[43,48],"decoder.":[49],"The":[50,196],"encoders":[51],"are":[52,64,202],"backboned":[53],"on":[54,139,161],"SwinV2":[55],"EfficientNet-B3,":[57],"which":[59],"extracted":[61],"features":[63],"aggregated":[65],"at":[66],"multiple":[67],"levels":[68],"using":[69],"bilateral":[71],"feature":[72,85],"fusion":[73],"module":[74,104],"(BiFFM).":[75],"First,":[76],"we":[77,98,117],"used":[78,99],"attention":[79,101],"gate":[80],"(ATG)":[81],"modules":[82],"to":[83,105,130],"enhance":[84],"representation,":[86],"improving":[87],"results":[89],"objects":[91],"with":[92,148],"various":[93],"shapes":[94],"sizes.":[96],"Second,":[97],"an":[100],"residual":[102],"(ATR)":[103],"refine":[106],"features\u2019s":[108],"learning,":[109],"alleviating":[110],"boundary":[111],"blurring":[112],"occluded":[114],"objects.":[115],"Finally,":[116],"developed":[118],"new":[120],"strategy,":[121],"called":[122],"auxiliary":[123],"supervise":[124],"strategy":[125],"(ASS),":[126],"model":[128,201],"optimization":[129],"further":[131],"improve":[132],"performance.":[134],"Our":[135],"method":[136,157,188],"was":[137],"tested":[138],"WHU,":[141],"Inria,":[142],"Potsdam":[144,174],"datasets,":[145],"compared":[147],"CNN-based":[149],"transformer-based":[151],"methods.":[152],"Results":[153],"showed":[154],"that":[155,180],"our":[156],"achieved":[158],"state-of-the-art":[159],"performance":[160],"WHU":[163],"building":[164,169],"dataset":[165,170,175],"(92.31%":[166],"IoU),":[167,172],"Inria":[168],"(83.71%":[171],"(80.27%":[176],"MIoU).":[177],"We":[178],"concluded":[179],"is":[182],"flexible,":[184],"robust,":[185],"effective":[187],"VHR":[194],"codes":[197],"proposed":[200],"available":[203],"athttps://github.com/long123524/CLCFormer.":[204]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":2}],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2025-10-10T00:00:00"}