{"id":"https://openalex.org/W4294310856","doi":"https://doi.org/10.1109/tgrs.2022.3203910","title":"A Mutual Information Domain Adaptation Network for Remotely Sensed Semantic Segmentation","display_name":"A Mutual Information Domain Adaptation Network for Remotely Sensed Semantic Segmentation","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4294310856","doi":"https://doi.org/10.1109/tgrs.2022.3203910"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3203910","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3203910","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/A5100332203","display_name":"Hongyu Chen","orcid":"https://orcid.org/0000-0002-5329-8854"},"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":"Hongyu Chen","raw_affiliation_strings":["State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-5329-8854","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I4210118728","https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100385579","display_name":"Hongyan Zhang","orcid":"https://orcid.org/0000-0002-7894-5755"},"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":"Hongyan Zhang","raw_affiliation_strings":["State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-7894-5755","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I4210118728","https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005906594","display_name":"Guangyi Yang","orcid":"https://orcid.org/0000-0002-1580-0188"},"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"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guangyi Yang","raw_affiliation_strings":["School of Electronic Information, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0002-1580-0188","affiliations":[{"raw_affiliation_string":"School of Electronic Information, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111003697","display_name":"Shengyang Li","orcid":"https://orcid.org/0000-0002-9888-9869"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210086028","display_name":"Technology and Engineering Center for Space Utilization","ror":"https://ror.org/00cn03n83","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210086028"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shengyang Li","raw_affiliation_strings":["Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9888-9869","affiliations":[{"raw_affiliation_string":"Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210086028","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100673818","display_name":"Liangpei Zhang","orcid":"https://orcid.org/0000-0001-6890-3650"},"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":"Liangpei Zhang","raw_affiliation_strings":["State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0001-6890-3650","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I4210118728","https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.8869,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.91991956,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"60","issue":null,"first_page":"1","last_page":"16"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.9947999715805054,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.994700014591217,"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/computer-science","display_name":"Computer science","score":0.8453718423843384},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7052550315856934},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6426388025283813},{"id":"https://openalex.org/keywords/mutual-information","display_name":"Mutual information","score":0.5406792163848877},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5237469673156738},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5000765323638916},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.47907429933547974},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.45725056529045105},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4375765919685364},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.43170610070228577}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8453718423843384},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7052550315856934},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6426388025283813},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.5406792163848877},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5237469673156738},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5000765323638916},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.47907429933547974},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.45725056529045105},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4375765919685364},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.43170610070228577},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2022.3203910","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3203910","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":[{"id":"https://metadata.un.org/sdg/17","score":0.5,"display_name":"Partnerships for the goals"}],"awards":[{"id":"https://openalex.org/G2918223366","display_name":null,"funder_award_id":"2020CFA053","funder_id":"https://openalex.org/F4320322186","funder_display_name":"Natural Science Foundation of Hubei Province"},{"id":"https://openalex.org/G5377590583","display_name":null,"funder_award_id":"42071322","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8145771212","display_name":null,"funder_award_id":"61871298","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"},{"id":"https://openalex.org/F4320322186","display_name":"Natural Science Foundation of Hubei Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2042873848","https://openalex.org/W2058445942","https://openalex.org/W2095389390","https://openalex.org/W2112546694","https://openalex.org/W2164943005","https://openalex.org/W2186500555","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2395611524","https://openalex.org/W2412782625","https://openalex.org/W2440384215","https://openalex.org/W2475287302","https://openalex.org/W2560023338","https://openalex.org/W2562192638","https://openalex.org/W2610614780","https://openalex.org/W2623546809","https://openalex.org/W2752782242","https://openalex.org/W2785825007","https://openalex.org/W2787091153","https://openalex.org/W2787931417","https://openalex.org/W2790713095","https://openalex.org/W2796048785","https://openalex.org/W2796813206","https://openalex.org/W2800903797","https://openalex.org/W2803168974","https://openalex.org/W2884585870","https://openalex.org/W2886742956","https://openalex.org/W2900147932","https://openalex.org/W2916798096","https://openalex.org/W2939571759","https://openalex.org/W2955058313","https://openalex.org/W2962687275","https://openalex.org/W2963073217","https://openalex.org/W2963091558","https://openalex.org/W2963107255","https://openalex.org/W2963378109","https://openalex.org/W2963446712","https://openalex.org/W2963881378","https://openalex.org/W2963958441","https://openalex.org/W2978725006","https://openalex.org/W2985409929","https://openalex.org/W2986564768","https://openalex.org/W2993235622","https://openalex.org/W2997195386","https://openalex.org/W2998115938","https://openalex.org/W2999725795","https://openalex.org/W3014055653","https://openalex.org/W3082576756","https://openalex.org/W3085433944","https://openalex.org/W3093701838","https://openalex.org/W3100006985","https://openalex.org/W3101468328","https://openalex.org/W3105043467","https://openalex.org/W3112503277","https://openalex.org/W3131937156","https://openalex.org/W3157967435","https://openalex.org/W3173206925","https://openalex.org/W4205379033","https://openalex.org/W4212841652","https://openalex.org/W6681588610","https://openalex.org/W6695692224","https://openalex.org/W6730623217","https://openalex.org/W6746282794","https://openalex.org/W6748486149","https://openalex.org/W6767127972","https://openalex.org/W6779751291"],"related_works":["https://openalex.org/W2036846997","https://openalex.org/W2112223184","https://openalex.org/W2347586617","https://openalex.org/W2161963661","https://openalex.org/W2125614474","https://openalex.org/W2295845123","https://openalex.org/W2358054814","https://openalex.org/W2090882960","https://openalex.org/W2889544313","https://openalex.org/W2165061339"],"abstract_inverted_index":{"Although":[0],"deep":[1,35],"learning":[2,36,79,89,95,204],"has":[3],"made":[4],"semantic":[5,73,97,131,187,194],"segmentation":[6,74,98,188,195],"of":[7,26,96,205],"very-high-resolution":[8],"(VHR)":[9],"remote":[10],"sensing":[11],"(RS)":[12],"images":[13],"practical":[14],"and":[15,31,99,146,157,175,216,227,232,252],"efficient,":[16],"its":[17],"large-scale":[18],"application":[19],"is":[20,126,184],"still":[21],"limited.":[22],"Given":[23],"the":[24,81,93,102,111,116,137,168,172,198,202,206,210,222],"diversity":[25],"imaging":[27],"sensors,":[28],"acquisition":[29],"conditions,":[30],"regional":[32],"styles,":[33],"a":[34,60,158,181],"network":[37,67],"well-trained":[38],"on":[39,110,221],"one":[40,124],"source":[41],"domain":[42,55,65,200,244],"dataset":[43],"often":[44],"suffers":[45],"from":[46],"drastic":[47],"performance":[48],"drops":[49],"when":[50],"applied":[51,127],"to":[52,128,142,166],"other":[53,242],"target":[54,199],"datasets.":[56,235],"Thus,":[57],"we":[58],"propose":[59],"novel":[61],"end-to-end":[62],"mutual":[63,159],"information":[64,118,160],"adaptation":[66,245],"(MIDANet)":[68],"that":[69,135,189,239],"can":[70,107],"shift":[71],"between":[72,171],"domains":[75],"by":[76,105],"integrating":[77],"multitask":[78],"in":[80,197,248],"convolutional":[82],"neural":[83],"networks":[84],"within":[85],"an":[86],"entropy":[87,208],"adversarial":[88,203],"(EAL)":[90],"framework.":[91],"Through":[92],"joint":[94],"elevation":[100,112],"estimation,":[101],"features":[103],"extracted":[104],"MIDANet":[106,183,240],"concentrate":[108],"more":[109],"clues":[113],"while":[114],"dropping":[115],"domain-variant":[117],"(i.e.,":[119],"texture,":[120],"spectral":[121],"information).":[122],"First,":[123],"encoder":[125],"excavate":[129],"general":[130],"features.":[132],"Two":[133],"decoders":[134],"share":[136],"same":[138],"architecture":[139],"are":[140,164],"used":[141],"perform":[143],"pixel-level":[144],"classification":[145,207],"digital":[147],"surface":[148],"model":[149],"(DSM)":[150],"regression.":[151],"Second,":[152],"feature":[153,178],"interaction":[154],"modules":[155],"(FIMs)":[156],"attention":[161],"unit":[162],"(MIAU)":[163],"designed":[165],"mine":[167],"latent":[169],"relationships":[170],"two":[173],"tasks":[174],"enhance":[176],"their":[177],"representations.":[179],"Finally,":[180],"final":[182],"obtained":[185],"for":[186,225],"does":[190],"not":[191],"require":[192],"any":[193],"labels":[196],"after":[201],"at":[209],"output":[211],"level.":[212],"Extensive":[213],"comparative":[214],"experiments":[215],"ablation":[217],"studies":[218],"were":[219],"conducted":[220],"International":[223],"Society":[224],"Photogrammetry":[226],"Remote":[228],"Sensing":[229],"(ISPRS)":[230],"Potsdam":[231],"Vaihingen":[233],"test":[234],"The":[236],"results":[237],"show":[238],"outperforms":[241],"state-of-the-art":[243],"(DA)":[246],"methods":[247],"both":[249],"evaluation":[250],"metrics":[251],"visual":[253],"assessment.":[254]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":6}],"updated_date":"2026-07-14T08:27:34.040176","created_date":"2025-10-10T00:00:00"}