{"id":"https://openalex.org/W3193649750","doi":"https://doi.org/10.1109/icip42928.2021.9506057","title":"Adapting Intra-Class Variations For Sar Image Classification","display_name":"Adapting Intra-Class Variations For Sar Image Classification","publication_year":2021,"publication_date":"2021-08-23","ids":{"openalex":"https://openalex.org/W3193649750","doi":"https://doi.org/10.1109/icip42928.2021.9506057","mag":"3193649750"},"language":"en","primary_location":{"id":"doi:10.1109/icip42928.2021.9506057","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip42928.2021.9506057","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5008117724","display_name":"Tsenjung Tai","orcid":null},"institutions":[{"id":"https://openalex.org/I118347220","display_name":"NEC (Japan)","ror":"https://ror.org/04jndar25","country_code":"JP","type":"company","lineage":["https://openalex.org/I118347220"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tsenjung Tai","raw_affiliation_strings":["NEC Corporation,Data Science Research Laboratories","Data Science Research Laboratories, NEC Corporation"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NEC Corporation,Data Science Research Laboratories","institution_ids":["https://openalex.org/I118347220"]},{"raw_affiliation_string":"Data Science Research Laboratories, NEC Corporation","institution_ids":["https://openalex.org/I118347220"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066517717","display_name":"Masato Toda","orcid":null},"institutions":[{"id":"https://openalex.org/I118347220","display_name":"NEC (Japan)","ror":"https://ror.org/04jndar25","country_code":"JP","type":"company","lineage":["https://openalex.org/I118347220"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masato Toda","raw_affiliation_strings":["NEC Corporation,Data Science Research Laboratories","Data Science Research Laboratories, NEC Corporation"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NEC Corporation,Data Science Research Laboratories","institution_ids":["https://openalex.org/I118347220"]},{"raw_affiliation_string":"Data Science Research Laboratories, NEC Corporation","institution_ids":["https://openalex.org/I118347220"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I118347220"],"apc_list":null,"apc_paid":null,"fwci":3.329,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.92034904,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9983999729156494,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9915000200271606,"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/T11609","display_name":"Geophysical Methods and Applications","score":0.9782000184059143,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.8257666826248169},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.7738887667655945},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.759955644607544},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7562259435653687},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7294250726699829},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6182896494865417},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.5405373573303223},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5294755697250366},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4977874755859375},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4669764041900635},{"id":"https://openalex.org/keywords/radar-imaging","display_name":"Radar imaging","score":0.44208037853240967},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4245636761188507},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.410910964012146},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3671599328517914},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.25280463695526123},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.2521955370903015}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.8257666826248169},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.7738887667655945},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.759955644607544},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7562259435653687},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7294250726699829},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6182896494865417},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.5405373573303223},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5294755697250366},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4977874755859375},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4669764041900635},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.44208037853240967},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4245636761188507},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.410910964012146},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3671599328517914},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.25280463695526123},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.2521955370903015},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip42928.2021.9506057","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip42928.2021.9506057","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7300000190734863,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1731081199","https://openalex.org/W2111847198","https://openalex.org/W2161381512","https://openalex.org/W2292481059","https://openalex.org/W2410591237","https://openalex.org/W2466539075","https://openalex.org/W2525330983","https://openalex.org/W2618177202","https://openalex.org/W2730249686","https://openalex.org/W2767699072","https://openalex.org/W2773828237","https://openalex.org/W2963080758","https://openalex.org/W2963118547","https://openalex.org/W2963327260","https://openalex.org/W2963532621","https://openalex.org/W2964159205","https://openalex.org/W2986381065","https://openalex.org/W3110080985","https://openalex.org/W6637618735","https://openalex.org/W6745620410","https://openalex.org/W6761661548"],"related_works":["https://openalex.org/W2404514746","https://openalex.org/W2546942002","https://openalex.org/W1964120219","https://openalex.org/W1835905048","https://openalex.org/W2905846897","https://openalex.org/W3102792585","https://openalex.org/W4327773867","https://openalex.org/W3193649750","https://openalex.org/W2889079306","https://openalex.org/W3013138473"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,32,42,47,79,106],"semi-supervised":[4],"domain":[5,109],"adaptation":[6],"(SSDA)":[7],"method":[8,122],"for":[9],"Synthetic":[10],"Aperture":[11],"Radar":[12],"(SAR)":[13],"image":[14,90,98],"classification.":[15],"SAR":[16,140],"imagery":[17],"is":[18,28,82],"important":[19],"in":[20,135],"ground":[21,141],"activity":[22],"monitoring,":[23],"but":[24],"its":[25],"wide":[26],"application":[27],"impeded":[29],"due":[30],"to":[31,46,59,73,84,117,133],"lack":[33],"of":[34,97,126],"annotations.":[35],"SSDA":[36,129],"methods":[37,53],"transfer":[38],"class-discriminative":[39],"knowledge":[40],"from":[41,86,131],"fully-labeled":[43],"source":[44,87],"dataset":[45],"scarcely-labeled":[48],"target":[49,61,108,119],"dataset.":[50,143],"However,":[51],"conventional":[52],"often":[54],"train":[55],"models":[56],"which":[57,104],"overfit":[58],"labeled":[60,118],"data":[62,88],"and":[63],"fail":[64],"on":[65,138],"unlabeled":[66],"data.":[67,120],"To":[68],"overcome":[69],"this,":[70],"we":[71],"propose":[72],"additionally":[74],"adapt":[75],"intra-class":[76],"variations.":[77],"Specifically,":[78],"conversion":[80,116],"network":[81],"trained":[83],"learn":[85],"the":[89,95,115,124,127,139],"feature":[91],"variations":[92],"caused":[93],"by":[94,113],"change":[96],"capturing":[99],"angle.":[100],"Then":[101],"synthetic":[102],"data,":[103],"represent":[105],"generalized":[107],"distribution,":[110],"are":[111],"estimated":[112],"applying":[114],"Our":[121],"improves":[123],"accuracy":[125],"state-of-the-art":[128],"approach":[130],"64.28%":[132],"80.40%":[134],"three-shot":[136],"cases":[137],"vehicle":[142]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}