{"id":"https://openalex.org/W4312272867","doi":"https://doi.org/10.1109/tgrs.2022.3225843","title":"Category-Wise Fusion and Enhancement Learning for Multimodal Remote Sensing Image Semantic Segmentation","display_name":"Category-Wise Fusion and Enhancement Learning for Multimodal Remote Sensing Image Semantic Segmentation","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4312272867","doi":"https://doi.org/10.1109/tgrs.2022.3225843"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3225843","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3225843","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","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/A5017558628","display_name":"Aihua Zheng","orcid":"https://orcid.org/0000-0002-9820-4743"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Aihua Zheng","raw_affiliation_strings":["Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Artificial Intelligence, Anhui University, Hefei, China","School of Artificial Intelligence, Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Artificial Intelligence, Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]},{"raw_affiliation_string":"School of Artificial Intelligence, Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065195263","display_name":"Jinbo He","orcid":"https://orcid.org/0000-0002-2785-9371"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinbo He","raw_affiliation_strings":["Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China","School of Computer Science and Technology, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]},{"raw_affiliation_string":"School of Computer Science and Technology, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050281636","display_name":"Ming Wang","orcid":"https://orcid.org/0000-0002-0512-637X"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Wang","raw_affiliation_strings":["Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China","School of Computer Science and Technology, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]},{"raw_affiliation_string":"School of Computer Science and Technology, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100398932","display_name":"Chenglong Li","orcid":"https://orcid.org/0000-0002-7233-2739"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenglong Li","raw_affiliation_strings":["Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Artificial Intelligence, Anhui University, Hefei, China","School of Artificial Intelligence, Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Artificial Intelligence, Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]},{"raw_affiliation_string":"School of Artificial Intelligence, Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107117636","display_name":"Bin Luo","orcid":"https://orcid.org/0000-0002-1414-3307"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Luo","raw_affiliation_strings":["Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China","School of Computer Science and Technology, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei, China"],"affiliations":[{"raw_affiliation_string":"Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]},{"raw_affiliation_string":"School of Computer Science and Technology, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University, Hefei, China","institution_ids":["https://openalex.org/I143868143"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5017558628"],"corresponding_institution_ids":["https://openalex.org/I143868143"],"apc_list":null,"apc_paid":null,"fwci":20.8988,"has_fulltext":false,"cited_by_count":205,"citation_normalized_percentile":{"value":0.99626333,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9994000196456909,"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":0.9994000196456909,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9923999905586243,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7179324626922607},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6635614633560181},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.66130131483078},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6408979296684265},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.6357250213623047},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.6323096752166748},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5982539653778076},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.570489764213562},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.480726033449173},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.469511479139328},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4689849019050598},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42940694093704224},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.059705764055252075}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7179324626922607},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6635614633560181},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.66130131483078},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6408979296684265},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.6357250213623047},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.6323096752166748},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5982539653778076},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.570489764213562},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.480726033449173},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.469511479139328},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4689849019050598},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42940694093704224},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.059705764055252075},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2022.3225843","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3225843","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2231845458","display_name":null,"funder_award_id":"61860206004","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3254430106","display_name":null,"funder_award_id":"61976002","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G458104311","display_name":null,"funder_award_id":"61976003","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7114861057","display_name":null,"funder_award_id":"U20B2068","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":63,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1937812750","https://openalex.org/W2108598243","https://openalex.org/W2194775991","https://openalex.org/W2527276685","https://openalex.org/W2560023338","https://openalex.org/W2563705555","https://openalex.org/W2593886839","https://openalex.org/W2623518586","https://openalex.org/W2630837129","https://openalex.org/W2755226765","https://openalex.org/W2765739551","https://openalex.org/W2787614951","https://openalex.org/W2793268137","https://openalex.org/W2793461576","https://openalex.org/W2803946774","https://openalex.org/W2804199516","https://openalex.org/W2804451008","https://openalex.org/W2811199523","https://openalex.org/W2814568980","https://openalex.org/W2893801697","https://openalex.org/W2899101283","https://openalex.org/W2899149423","https://openalex.org/W2908320224","https://openalex.org/W2943325261","https://openalex.org/W2946862972","https://openalex.org/W2955058313","https://openalex.org/W2956134899","https://openalex.org/W2956586819","https://openalex.org/W2963351448","https://openalex.org/W2963378109","https://openalex.org/W2963406768","https://openalex.org/W2963659230","https://openalex.org/W2963995737","https://openalex.org/W2972540077","https://openalex.org/W2991488782","https://openalex.org/W3028752951","https://openalex.org/W3034427230","https://openalex.org/W3034534947","https://openalex.org/W3034552520","https://openalex.org/W3048631361","https://openalex.org/W3102692100","https://openalex.org/W3104282073","https://openalex.org/W3128103554","https://openalex.org/W3129042754","https://openalex.org/W3138136606","https://openalex.org/W3138516171","https://openalex.org/W3161825146","https://openalex.org/W3168495321","https://openalex.org/W3183174367","https://openalex.org/W3184761517","https://openalex.org/W3198391933","https://openalex.org/W3203482237","https://openalex.org/W4205138939","https://openalex.org/W4287556986","https://openalex.org/W6639824700","https://openalex.org/W6727577265","https://openalex.org/W6739696289","https://openalex.org/W6786609286","https://openalex.org/W6790679990","https://openalex.org/W6798387234","https://openalex.org/W6948037097"],"related_works":["https://openalex.org/W2788731446","https://openalex.org/W2204403038","https://openalex.org/W3152170969","https://openalex.org/W2379054866","https://openalex.org/W2549658594","https://openalex.org/W2370195708","https://openalex.org/W1490651872","https://openalex.org/W2139242969","https://openalex.org/W2284201331","https://openalex.org/W2095903272"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,79,99],"simple":[4,100],"yet":[5,101],"effective":[6,22,102],"method":[7],"called":[8],"Category-wise":[9],"Fusion":[10],"and":[11,25,151,161],"Enhancement":[12],"learning":[13,105,134,153],"(CaFE),":[14],"which":[15],"leverages":[16],"the":[17,39,44,48,53,56,60,75,86,93,111,119,127,133,140,158,162,176],"category":[18,62,115],"priors":[19],"to":[20,46,65],"achieve":[21,47],"feature":[23,40,57,149],"fusion":[24,41,50,58,71,76,150],"imbalance":[26,95,152],"learning,":[27],"for":[28,113],"multi-modal":[29],"remote":[30],"sensing":[31],"image":[32],"semantic":[33],"segmentation.":[34],"In":[35,107],"particular,":[36,108],"we":[37,97,109],"disentangle":[38],"process":[42],"via":[43],"categories":[45],"category-wise":[49,103,148],"based":[51,117],"on":[52,88,118,139,171],"fact":[54],"that":[55,146],"in":[59,123,157],"same":[61],"regions":[63,141],"tends":[64],"have":[66],"similar":[67],"characteristics.":[68],"The":[69],"disentangled":[70],"would":[72,136],"also":[73],"increase":[74],"capacity":[77],"with":[78,142],"small":[80],"number":[81],"of":[82,121,178],"parameters":[83],"while":[84],"reducing":[85],"dependence":[87],"large-scale":[89],"training":[90,159],"data.":[91],"For":[92],"sample":[94],"problem,":[96],"design":[98],"enhancement":[104],"scheme.":[106],"assign":[110],"weight":[112],"each":[114],"region":[116,125],"proportion":[120],"samples":[122],"this":[124,131],"over":[126],"whole":[128],"image.":[129],"By":[130],"way,":[132],"algorithm":[135],"focus":[137],"more":[138],"smaller":[143],"proportion.":[144],"Note":[145],"both":[147],"are":[154],"only":[155],"performed":[156],"stage,":[160],"segmentation":[163],"efficiency":[164],"is":[165],"thus":[166],"not":[167],"affected.":[168],"Experimental":[169],"results":[170],"two":[172],"benchmark":[173],"datasets":[174],"demonstrate":[175],"effectiveness":[177],"our":[179],"CaFE":[180],"against":[181],"other":[182],"state-of-the-art":[183],"methods.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":20},{"year":2025,"cited_by_count":73},{"year":2024,"cited_by_count":54},{"year":2023,"cited_by_count":45},{"year":2022,"cited_by_count":13}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}