{"id":"https://openalex.org/W2971884209","doi":"https://doi.org/10.1109/lgrs.2019.2936897","title":"EM Simulation-Aided Zero-Shot Learning for SAR Automatic Target Recognition","display_name":"EM Simulation-Aided Zero-Shot Learning for SAR Automatic Target Recognition","publication_year":2019,"publication_date":"2019-09-05","ids":{"openalex":"https://openalex.org/W2971884209","doi":"https://doi.org/10.1109/lgrs.2019.2936897","mag":"2971884209"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2019.2936897","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2019.2936897","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/A5070033347","display_name":"Qian Song","orcid":"https://orcid.org/0000-0003-2746-6858"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qian Song","raw_affiliation_strings":["Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075156780","display_name":"Hui Chen","orcid":"https://orcid.org/0000-0002-9817-0002"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Chen","raw_affiliation_strings":["State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071461704","display_name":"Feng Xu","orcid":"https://orcid.org/0000-0002-7015-1467"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Xu","raw_affiliation_strings":["Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009237669","display_name":"Tie Jun Cui","orcid":"https://orcid.org/0000-0002-5862-1497"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tie Jun Cui","raw_affiliation_strings":["State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5070033347"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":6.7438,"has_fulltext":false,"cited_by_count":48,"citation_normalized_percentile":{"value":0.96410789,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"17","issue":"6","first_page":"1092","last_page":"1096"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9994999766349792,"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/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9936000108718872,"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/automatic-target-recognition","display_name":"Automatic target recognition","score":0.880807101726532},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7875266075134277},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7304986715316772},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.6913243532180786},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6715050339698792},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5605989694595337},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.5233891606330872},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.476365327835083},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.46467193961143494},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.46094805002212524},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4170486629009247},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4140549898147583},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.08348652720451355}],"concepts":[{"id":"https://openalex.org/C117623542","wikidata":"https://www.wikidata.org/wiki/Q621974","display_name":"Automatic target recognition","level":3,"score":0.880807101726532},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7875266075134277},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7304986715316772},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.6913243532180786},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6715050339698792},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5605989694595337},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.5233891606330872},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.476365327835083},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.46467193961143494},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.46094805002212524},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4170486629009247},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4140549898147583},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.08348652720451355},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2019.2936897","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2019.2936897","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"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7300000190734863,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G1812488959","display_name":null,"funder_award_id":"61571134","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7319655158","display_name":null,"funder_award_id":"2017YFB0502703","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G7930060216","display_name":null,"funder_award_id":"61822107","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/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W2003723718","https://openalex.org/W2004376198","https://openalex.org/W2006114067","https://openalex.org/W2022459032","https://openalex.org/W2117539524","https://openalex.org/W2123024445","https://openalex.org/W2187089797","https://openalex.org/W2308529009","https://openalex.org/W2410591237","https://openalex.org/W2475287302","https://openalex.org/W2607666785","https://openalex.org/W2754361766","https://openalex.org/W2767966434","https://openalex.org/W2773828237","https://openalex.org/W2890732922","https://openalex.org/W2900734375","https://openalex.org/W2901459322","https://openalex.org/W2962778872","https://openalex.org/W2962835968","https://openalex.org/W2964043336","https://openalex.org/W6637373629","https://openalex.org/W6678470764"],"related_works":["https://openalex.org/W3137365474","https://openalex.org/W2886347302","https://openalex.org/W2596763562","https://openalex.org/W2964218010","https://openalex.org/W2784759481","https://openalex.org/W1545594509","https://openalex.org/W2540523933","https://openalex.org/W3038591045","https://openalex.org/W3130755980","https://openalex.org/W2540650467"],"abstract_inverted_index":{"A":[0],"zero-shot":[1,199],"learning":[2],"(ZSL)":[3],"method":[4,177],"of":[5,26,37,46,136],"automatic":[6],"target":[7,29,48,180],"recognition":[8,181],"(ATR)":[9],"in":[10,63,88],"synthetic":[11],"aperture":[12],"radar":[13],"(SAR)":[14],"image":[15,69],"is":[16,30,124,166],"proposed":[17],"to":[18],"address":[19],"the":[20,38,47,56,64,67,77,121,141,147,156],"scenario,":[21],"where":[22],"no":[23],"SAR":[24],"sample":[25],"a":[27,71,103,112,127,167],"particular":[28],"available":[31],"for":[32,117,131,163,170],"training.":[33],"To":[34,93],"learn":[35],"features":[36],"unseen":[39],"target,":[40],"physics-based":[41],"electromagnetic":[42],"(EM)":[43],"simulated":[44,68,148],"images":[45,79,98],"under":[49],"different":[50],"azimuth":[51],"angles":[52],"are":[53,99],"used":[54],"as":[55],"training":[57],"data":[58,185],"instead.":[59],"The":[60,133],"challenge":[61],"lies":[62],"fact":[65],"that":[66,76,140],"has":[70],"distinct":[72],"but":[73],"nonessential":[74,104,142],"texture":[75],"real":[78],"do":[80],"not":[81],"have":[82],"and,":[83],"thus,":[84],"can":[85,145],"easily":[86],"result":[87],"an":[89],"overfitted":[90],"discriminator":[91],"network.":[92],"overcome":[94],"this":[95],"problem,":[96],"all":[97],"first":[100],"preprocessed":[101],"with":[102,150],"factor":[105,143],"suppression":[106,144],"step":[107],"and":[108,159,187,195],"then":[109],"fed":[110,125],"into":[111,126],"pretrained":[113],"convolutional":[114],"neural":[115],"network":[116,130],"feature":[118,122,137],"extraction.":[119],"Finally,":[120],"vector":[123],"trainable":[128],"fully-connected":[129],"classification.":[132],"low-dimensional":[134],"embedding":[135],"vectors":[138],"suggests":[139],"align":[146],"samples":[149,152],"true":[151],"effectively.":[153],"We":[154,174],"propose":[155],"max-tolerability":[157],"principle":[158],"averaged":[160],"margin":[161],"index":[162],"ZSL,":[164],"which":[165],"useful":[168],"indicator":[169],"selecting":[171],"optimal":[172],"classifier.":[173],"validated":[175],"our":[176],"on":[178,183,191,198],"ten-type":[179],"task":[182],"MSTAR":[184],"sets":[186],"achieved":[188],"91.93%":[189],"accuracy":[190,197],"nine":[192],"known":[193],"targets":[194],"79.08%":[196],"target.":[200]},"counts_by_year":[{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
