{"id":"https://openalex.org/W3102923495","doi":"https://doi.org/10.1109/lgrs.2020.3036585","title":"A Bayesian Approach to Active Self-Paced Deep Learning for SAR Automatic Target Recognition","display_name":"A Bayesian Approach to Active Self-Paced Deep Learning for SAR Automatic Target Recognition","publication_year":2020,"publication_date":"2020-11-17","ids":{"openalex":"https://openalex.org/W3102923495","doi":"https://doi.org/10.1109/lgrs.2020.3036585","mag":"3102923495"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2020.3036585","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3036585","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/A5032781096","display_name":"Haohao Ren","orcid":"https://orcid.org/0000-0001-5022-393X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haohao Ren","raw_affiliation_strings":["School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0001-5022-393X","affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101900861","display_name":"Xuelian Yu","orcid":"https://orcid.org/0000-0002-9577-3238"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuelian Yu","raw_affiliation_strings":["School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-9577-3238","affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006095323","display_name":"Lorenzo Bruzzone","orcid":"https://orcid.org/0000-0002-6036-459X"},"institutions":[{"id":"https://openalex.org/I193223587","display_name":"University of Trento","ror":"https://ror.org/05trd4x28","country_code":"IT","type":"education","lineage":["https://openalex.org/I193223587"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Lorenzo Bruzzone","raw_affiliation_strings":["Department of Information Engineering and Computer Science, University of Trento, Trento, Italy"],"raw_orcid":"https://orcid.org/0000-0002-6036-459X","affiliations":[{"raw_affiliation_string":"Department of Information Engineering and Computer Science, University of Trento, Trento, Italy","institution_ids":["https://openalex.org/I193223587"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100717545","display_name":"Yukun Zhang","orcid":"https://orcid.org/0000-0002-7283-5678"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yukun Zhang","raw_affiliation_strings":["School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0002-7283-5678","affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100743863","display_name":"Lin Zou","orcid":"https://orcid.org/0000-0001-7880-2992"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Zou","raw_affiliation_strings":["School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0001-7880-2992","affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050556614","display_name":"Xuegang Wang","orcid":"https://orcid.org/0000-0001-9008-5914"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuegang Wang","raw_affiliation_strings":["School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":"https://orcid.org/0000-0001-9008-5914","affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":28.6893,"has_fulltext":false,"cited_by_count":29,"citation_normalized_percentile":{"value":0.99183781,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9998999834060669,"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":0.9998999834060669,"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/T11609","display_name":"Geophysical Methods and Applications","score":0.9951000213623047,"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/T11698","display_name":"Underwater Acoustics Research","score":0.9947999715805054,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/automatic-target-recognition","display_name":"Automatic target recognition","score":0.8048855066299438},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8024625778198242},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7713310718536377},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6634030342102051},{"id":"https://openalex.org/keywords/target-acquisition","display_name":"Target acquisition","score":0.5965074896812439},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.5898884534835815},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.585896372795105},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5526556968688965},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5464044213294983},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4278803765773773},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.42073625326156616},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4193105101585388},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11083707213401794}],"concepts":[{"id":"https://openalex.org/C117623542","wikidata":"https://www.wikidata.org/wiki/Q621974","display_name":"Automatic target recognition","level":3,"score":0.8048855066299438},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8024625778198242},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7713310718536377},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6634030342102051},{"id":"https://openalex.org/C2779726219","wikidata":"https://www.wikidata.org/wiki/Q7685884","display_name":"Target acquisition","level":2,"score":0.5965074896812439},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.5898884534835815},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.585896372795105},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5526556968688965},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5464044213294983},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4278803765773773},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.42073625326156616},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4193105101585388},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11083707213401794},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/lgrs.2020.3036585","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3036585","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"},{"id":"pmh:oai:iris.unitn.it:11572/401516","is_oa":false,"landing_page_url":"https://hdl.handle.net/11572/401516","pdf_url":null,"source":{"id":"https://openalex.org/S4377196320","display_name":"Iris (University of Trento)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I193223587","host_organization_name":"University of Trento","host_organization_lineage":["https://openalex.org/I193223587"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G231933381","display_name":null,"funder_award_id":"61806046","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W601603264","https://openalex.org/W1567512734","https://openalex.org/W2035054708","https://openalex.org/W2410591237","https://openalex.org/W2471138382","https://openalex.org/W2520774990","https://openalex.org/W2575032143","https://openalex.org/W2588453093","https://openalex.org/W2759213214","https://openalex.org/W2790279720","https://openalex.org/W2809113079","https://openalex.org/W2883279772","https://openalex.org/W2889773939","https://openalex.org/W2897760800","https://openalex.org/W2944003962","https://openalex.org/W2965344373","https://openalex.org/W2990867745","https://openalex.org/W6618476874"],"related_works":["https://openalex.org/W3137365474","https://openalex.org/W2784759481","https://openalex.org/W3038591045","https://openalex.org/W2106749053","https://openalex.org/W2053024573","https://openalex.org/W3130755980","https://openalex.org/W2540523933","https://openalex.org/W4380446815","https://openalex.org/W2773828237","https://openalex.org/W4386323663"],"abstract_inverted_index":{"Deep":[0],"learning":[1,51,76,123,142],"has":[2],"attracted":[3],"intensive":[4],"attention":[5],"in":[6,81],"synthetic":[7],"aperture":[8],"radar":[9],"(SAR)":[10],"automatic":[11],"target":[12,182],"recognition":[13,185],"(ATR).":[14],"Usually,":[15],"a":[16,26,58,77,85,92,108,157,161],"considerable":[17],"number":[18,87],"of":[19,37,69,84,88,110,152],"labeled":[20,89,171,202],"samples":[21,106,112,117,136,172,203],"are":[22,118,137],"necessary":[23],"to":[24,54,99],"learn":[25],"deep":[27,50,70],"model":[28,71,80,154],"for":[29,113],"obtaining":[30],"good":[31],"generalization":[32],"capability.":[33],"However,":[34],"the":[35,63,67,82,104,130,150,153,178,191],"process":[36,68],"sample":[38,94],"labeling":[39],"is":[40,97,166],"time-consuming":[41],"and":[42,101,126,144,180,184],"costly.":[43],"This":[44],"letter":[45],"proposes":[46],"an":[47,140],"active":[48,141],"self-paced":[49,122],"(ASPDL)":[52],"approach":[53],"SAR":[55],"ATR.":[56],"In":[57],"nutshell,":[59],"we":[60,148],"first":[61],"introduce":[62],"Bayesian":[64],"inference":[65],"into":[66],"parameter":[72],"optimization,":[73],"aiming":[74],"at":[75],"robust":[78],"classification":[79,132,197],"case":[83],"limited":[86],"samples.":[90],"Next,":[91],"cost-effective":[93],"selection":[95],"strategy":[96,143],"presented":[98],"iteratively":[100],"actively":[102,119],"select":[103],"informative":[105],"from":[107],"pool":[109],"unlabeled":[111],"labeling.":[114],"Concretely,":[115],"high-confidence":[116],"selected":[120],"through":[121,139],"(SPL)":[124],"way":[125],"automatically":[127],"pseudo-labeled":[128],"with":[129,173,199,205],"current":[131],"model,":[133],"whereas":[134],"low-confidence":[135],"chosen":[138],"manually":[145],"labeled.":[146],"Finally,":[147],"update":[149],"parameters":[151],"by":[155,168],"minimizing":[156],"dual-loss":[158],"function":[159],"using":[160],"new":[162,170],"training":[163],"set":[164],"that":[165,190],"constructed":[167],"incorporating":[169],"original":[174],"ones.":[175],"Experiments":[176],"on":[177],"moving":[179],"stationary":[181],"acquisition":[183],"(MSTAR)":[186],"benchmark":[187],"data":[188],"demonstrate":[189],"proposed":[192],"method":[193],"can":[194],"achieve":[195],"better":[196],"accuracy":[198],"relatively":[200],"few":[201],"compared":[204],"some":[206],"state-of-the-art":[207],"methods.":[208]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":5}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}