{"id":"https://openalex.org/W4402473932","doi":"https://doi.org/10.1109/tgrs.2024.3458937","title":"Unsupervised Domain Adaptation for Ship Classification via Progressive Feature Alignment: From Optical to SAR Images","display_name":"Unsupervised Domain Adaptation for Ship Classification via Progressive Feature Alignment: From Optical to SAR Images","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4402473932","doi":"https://doi.org/10.1109/tgrs.2024.3458937"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2024.3458937","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3458937","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/A5035630827","display_name":"Yu Shi","orcid":"https://orcid.org/0000-0002-5081-231X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Shi","raw_affiliation_strings":["National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0002-5081-231X","affiliations":[{"raw_affiliation_string":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100716826","display_name":"Lan Du","orcid":"https://orcid.org/0000-0002-4503-0022"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lan Du","raw_affiliation_strings":["National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0002-4503-0022","affiliations":[{"raw_affiliation_string":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101640370","display_name":"Yuchen Guo","orcid":"https://orcid.org/0000-0003-3424-7231"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuchen Guo","raw_affiliation_strings":["Academy of Advanced Interdisciplinary Research, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0003-3424-7231","affiliations":[{"raw_affiliation_string":"Academy of Advanced Interdisciplinary Research, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061315570","display_name":"Yuang Du","orcid":"https://orcid.org/0000-0002-6525-7056"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuang Du","raw_affiliation_strings":["National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0002-6525-7056","affiliations":[{"raw_affiliation_string":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086376207","display_name":"Yiming Li","orcid":"https://orcid.org/0009-0006-5948-0442"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiming Li","raw_affiliation_strings":["National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0009-0006-5948-0442","affiliations":[{"raw_affiliation_string":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":4.2141,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.94692548,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"62","issue":null,"first_page":"1","last_page":"17"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11698","display_name":"Underwater Acoustics Research","score":0.9789000153541565,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11698","display_name":"Underwater Acoustics Research","score":0.9789000153541565,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.926800012588501,"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/T12624","display_name":"Maritime and Coastal Archaeology","score":0.9124000072479248,"subfield":{"id":"https://openalex.org/subfields/1204","display_name":"Archeology"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6429873704910278},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.6361279487609863},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"Domain adaptation","score":0.58127361536026},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5791963338851929},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5789117813110352},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5745073556900024},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5582877397537231},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4894603490829468},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.48049604892730713},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4198305308818817},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3496819734573364},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.26546669006347656},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.2330288589000702},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09843587875366211}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6429873704910278},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.6361279487609863},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.58127361536026},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5791963338851929},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5789117813110352},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5745073556900024},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5582877397537231},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4894603490829468},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.48049604892730713},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4198305308818817},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3496819734573364},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.26546669006347656},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.2330288589000702},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09843587875366211},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2024.3458937","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3458937","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/G241050578","display_name":null,"funder_award_id":"62201431","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4987181584","display_name":null,"funder_award_id":"U21B2039","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/F4320327912","display_name":"Higher Education Discipline Innovation Project","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W2104094955","https://openalex.org/W2120066054","https://openalex.org/W2131953535","https://openalex.org/W2165698076","https://openalex.org/W2194775991","https://openalex.org/W2752788177","https://openalex.org/W2770553941","https://openalex.org/W2909500413","https://openalex.org/W2944029760","https://openalex.org/W2962793481","https://openalex.org/W2963532621","https://openalex.org/W2978573218","https://openalex.org/W2981798583","https://openalex.org/W2990641522","https://openalex.org/W3002497127","https://openalex.org/W3012162230","https://openalex.org/W3021632667","https://openalex.org/W3035519852","https://openalex.org/W3044749031","https://openalex.org/W3081765370","https://openalex.org/W3096831136","https://openalex.org/W3106919514","https://openalex.org/W3128476715","https://openalex.org/W3133008479","https://openalex.org/W3166529525","https://openalex.org/W3169512507","https://openalex.org/W3175370657","https://openalex.org/W3198651093","https://openalex.org/W3210839290","https://openalex.org/W4200495849","https://openalex.org/W4226456028","https://openalex.org/W4285190288","https://openalex.org/W4285253304","https://openalex.org/W4312108898","https://openalex.org/W4312381878","https://openalex.org/W4312627428","https://openalex.org/W4313156423","https://openalex.org/W4316039740","https://openalex.org/W4352976976","https://openalex.org/W4385153796","https://openalex.org/W4385450714","https://openalex.org/W4385901036","https://openalex.org/W4386590621","https://openalex.org/W4386869711","https://openalex.org/W4388624495","https://openalex.org/W4389067838","https://openalex.org/W4391104028","https://openalex.org/W4392399500","https://openalex.org/W6639480849","https://openalex.org/W6683633756","https://openalex.org/W6762913911","https://openalex.org/W6773005947","https://openalex.org/W6779101013","https://openalex.org/W6788329692","https://openalex.org/W6868435136"],"related_works":["https://openalex.org/W4389474468","https://openalex.org/W4300172004","https://openalex.org/W4321649381","https://openalex.org/W2997645659","https://openalex.org/W3180787869","https://openalex.org/W3203792196","https://openalex.org/W2955455867","https://openalex.org/W4295929828","https://openalex.org/W3156096827","https://openalex.org/W4210966920"],"abstract_inverted_index":{"This":[0],"article":[1,119],"delves":[2],"into":[3,199],"the":[4,27,57,68,101,105,115,148,153,158,180,183,191,196,200,209,221,226,255],"topic":[5],"of":[6,71,104,186,190,202,225,258,274],"unsupervised":[7,272],"domain":[8,18,107,143,149,168],"adaptation":[9],"(UDA)":[10],"by":[11,30,81],"transferring":[12],"knowledge":[13],"from":[14,87,157],"rich":[15],"labeled":[16,40,82],"optical":[17,64,83,133,164],"to":[19,56,96,151,173,194,219,253],"unlabeled":[20,135],"synthetic":[21],"aperture":[22],"radar":[23],"(SAR)":[24],"domain,":[25],"tackling":[26],"issues":[28],"faced":[29],"deep-learning-based":[31],"SAR":[32,41,66,125,136,162,227,259,275],"ship":[33,126],"classification":[34,111,127,273],"methods":[35,45],"that":[36,265],"rely":[37],"on":[38],"abundant":[39],"images.":[42],"Typical":[43],"UDA":[44,122],"usually":[46],"extract":[47],"domain-invariant":[48],"representations":[49],"(DIRs)":[50],"between":[51,63,132,161],"two":[52,140],"domains.":[53],"However,":[54],"due":[55],"prominent":[58,159],"differences":[59,160],"in":[60,271],"imaging":[61],"mechanisms":[62],"and":[65,134,144,163,182],"images,":[67,165],"discriminative":[69],"characteristics":[70],"same":[72,192],"classes":[73],"across":[74,142],"domains":[75,141],"may":[76],"vary.":[77],"Feature":[78],"representation":[79,198],"guided":[80],"images":[84],"therefore":[85],"suffers":[86],"a":[88,121,205,249],"particularly":[89],"serious":[90],"source-bias":[91],"problem,":[92],"making":[93],"DIR":[94,176],"difficult":[95],"be":[97],"extracted.":[98],"Moreover,":[99],"capturing":[100],"category":[102,223],"structure":[103,224,257],"target":[106],"is":[108,171],"crucial":[109],"for":[110,124,236],"tasks.":[112],"To":[113],"solve":[114],"above":[116],"challenges,":[117],"this":[118],"proposes":[120],"framework":[123],"via":[128],"progressive":[129],"feature":[130,166,214],"alignment":[131,169,217],"domains,":[137],"gradually":[138],"aligning":[139],"class":[145,210,216,256],"levels.":[146],"At":[147,208],"level,":[150,211],"reduce":[152],"transfer":[154],"difficulty":[155],"stemming":[156],"calibrated":[167],"(FCDA)":[170],"presented":[172],"achieve":[174],"accurate":[175],"extraction.":[177],"FCDA":[178],"combines":[179],"reconstruction":[181],"consistency":[184,250],"constraints":[185],"different":[187],"perturbed":[188],"versions":[189],"image":[193],"calibrate":[195],"optical-bias":[197],"features":[201],"unbiased":[203],"toward":[204],"specific":[206],"domain.":[207,228,260],"we":[212],"proposed":[213],"enhanced":[215],"(FECA)":[218],"capture":[220],"fine-grained":[222],"FECA":[229],"incorporates":[230],"pseudo-label-based":[231],"cross-domain":[232,245],"contrastive":[233],"learning":[234,251],"(CDC)":[235],"intraclass":[237],"compactness":[238],"as":[239,241],"well":[240],"interclass":[242],"separation":[243],"among":[244],"categories,":[246],"along":[247],"with":[248],"approach":[252],"enhance":[254],"The":[261],"experimental":[262],"results":[263],"indicate":[264],"our":[266],"method":[267],"achieves":[268],"exceptional":[269],"performance":[270],"ships.":[276]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":9}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}