{"id":"https://openalex.org/W3130848852","doi":"https://doi.org/10.1109/igarss39084.2020.9324364","title":"SAR Target Classification with Limited Data via Data Driven Active Learning","display_name":"SAR Target Classification with Limited Data via Data Driven Active Learning","publication_year":2020,"publication_date":"2020-09-26","ids":{"openalex":"https://openalex.org/W3130848852","doi":"https://doi.org/10.1109/igarss39084.2020.9324364","mag":"3130848852"},"language":"en","primary_location":{"id":"doi:10.1109/igarss39084.2020.9324364","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9324364","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","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/A5012286535","display_name":"Yue Zhou","orcid":"https://orcid.org/0000-0002-3080-6721"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Zhou","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074129836","display_name":"Xue Jiang","orcid":"https://orcid.org/0000-0001-7099-6817"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xue Jiang","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101581055","display_name":"Zhou Li","orcid":"https://orcid.org/0000-0002-2708-1694"},"institutions":[{"id":"https://openalex.org/I4210166112","display_name":"State Key Laboratory of Remote Sensing Science","ror":"https://ror.org/05wzjqa24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210166112"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhou Li","raw_affiliation_strings":["Beijing Institute of Remote Sensing Information, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Remote Sensing Information, Beijing, China","institution_ids":["https://openalex.org/I4210166112"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5061319150","display_name":"Xingzhao Liu","orcid":"https://orcid.org/0000-0002-4533-3904"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingzhao Liu","raw_affiliation_strings":["Shanghai Jiao Tong University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University, Shanghai, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2475","last_page":"2478"},"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":0.9969000220298767,"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":0.9969000220298767,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9966999888420105,"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9965999722480774,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8135017156600952},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7107206583023071},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7053114771842957},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.6051203608512878},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.579014778137207},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5752755403518677},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5542115569114685},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.5142418742179871},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.5026051998138428},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4990718364715576},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4918687343597412},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.47499528527259827},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4670775234699249},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.44571566581726074},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.41041356325149536},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3695628046989441},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.17974445223808289},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.08679118752479553}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8135017156600952},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7107206583023071},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7053114771842957},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.6051203608512878},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.579014778137207},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5752755403518677},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5542115569114685},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.5142418742179871},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.5026051998138428},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4990718364715576},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4918687343597412},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.47499528527259827},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4670775234699249},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.44571566581726074},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.41041356325149536},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3695628046989441},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17974445223808289},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.08679118752479553},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss39084.2020.9324364","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9324364","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.550000011920929},{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.41999998688697815}],"awards":[{"id":"https://openalex.org/G2463320017","display_name":null,"funder_award_id":"61971279","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":14,"referenced_works":["https://openalex.org/W1540007258","https://openalex.org/W2008989859","https://openalex.org/W2116789252","https://openalex.org/W2117539524","https://openalex.org/W2133504205","https://openalex.org/W2144452292","https://openalex.org/W2148143831","https://openalex.org/W2163605009","https://openalex.org/W2165484066","https://openalex.org/W2194775991","https://openalex.org/W2347336074","https://openalex.org/W2410591237","https://openalex.org/W2765407302","https://openalex.org/W6653695713"],"related_works":["https://openalex.org/W2361861616","https://openalex.org/W2263699433","https://openalex.org/W2377979023","https://openalex.org/W2218034408","https://openalex.org/W2392921965","https://openalex.org/W2358755282","https://openalex.org/W2949671220","https://openalex.org/W1492505081","https://openalex.org/W4312364074","https://openalex.org/W4389518428"],"abstract_inverted_index":{"With":[0],"the":[1,31,47,61,64,69,75,88,110,144,157,195,198,202,215,218,222,234],"rapid":[2],"development":[3],"of":[4,40,49,63,78,82,112,125,197,204,217,233],"deep":[5,10,22,91],"learning,":[6],"more":[7,9],"and":[8,66],"neural":[11,92],"networks":[12,93],"with":[13,229],"strong":[14],"discrimination":[15],"have":[16],"come":[17],"up.":[18],"One":[19],"reason":[20],"why":[21],"learning":[23,146,170],"models":[24],"can":[25,193,226],"achieve":[26],"such":[27,37],"good":[28],"results":[29,209],"is":[30,43],"abundant":[32],"annotation":[33,41],"data.":[34],"However,":[35],"obtaining":[36],"considerable":[38],"amount":[39,77],"data":[42,81,105,190,206],"costly,":[44],"especially":[45],"in":[46,68,120],"field":[48],"synthetic":[50],"aperture":[51],"radar":[52],"(SAR).":[53],"High-quality":[54],"SAR":[55,148],"dataset":[56,213],"cannot":[57],"be":[58,227],"constructed":[59],"without":[60],"support":[62],"specialists":[65,155],"institutes":[67],"related":[70],"field,":[71],"which":[72,151,177],"leads":[73],"to":[74,86,108,115,153,184,189],"limited":[76,98],"labeled":[79,99,113],"training":[80,100,127,137,205,236],"SAR.":[83],"In":[84,139,187],"order":[85],"solve":[87],"problem":[89],"that":[90,159],"(DNNs)":[94],"may":[95],"face":[96],"under":[97],"data,":[101],"researchers":[102],"often":[103],"use":[104],"augmentation":[106],"methods":[107],"increase":[109],"number":[111,124,203],"samples":[114,128,158],"boost":[116],"model":[117,199],"performance.":[118],"But":[119],"fact,":[121],"a":[122,167],"large":[123],"augmented":[126],"not":[129,179],"only":[130,230],"introduce":[131,143],"extra":[132],"noise,":[133],"but":[134],"also":[135],"additional":[136],"time.":[138],"this":[140],"paper,":[141],"we":[142,165],"active":[145,169],"into":[147],"target":[149],"recognition,":[150],"used":[152],"help":[154],"select":[156,185],"are":[160],"most":[161],"worth":[162],"labeling.":[163],"Moreover,":[164],"propose":[166],"data-driven":[168],"scheme":[171],"named":[172],"Ranking":[173],"Loss":[174],"Module":[175],"(RLM),":[176],"does":[178],"rely":[180],"on":[181,211],"artificially":[182],"strategies":[183],"samples.":[186,207,237],"contrast":[188],"augmentation,":[191],"it":[192],"improve":[194],"performance":[196,225],"while":[200],"reducing":[201],"Experimental":[208],"based":[210],"MSTAR":[212],"demonstrate":[214],"superiority":[216],"proposed":[219],"scheme.":[220],"Using":[221],"RLM,":[223],"better":[224],"achieved":[228],"one":[231],"third":[232],"full":[235]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}