{"id":"https://openalex.org/W3200139395","doi":"https://doi.org/10.1109/lgrs.2021.3109342","title":"Novel Loss Function in CNN for Small Sample Target Recognition in SAR Images","display_name":"Novel Loss Function in CNN for Small Sample Target Recognition in SAR Images","publication_year":2021,"publication_date":"2021-09-15","ids":{"openalex":"https://openalex.org/W3200139395","doi":"https://doi.org/10.1109/lgrs.2021.3109342","mag":"3200139395"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2021.3109342","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2021.3109342","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/A5060425100","display_name":"Xiaoyan Zhou","orcid":"https://orcid.org/0009-0002-3471-0441"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyan Zhou","raw_affiliation_strings":["State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084996912","display_name":"Tao Tang","orcid":"https://orcid.org/0000-0002-9071-137X"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Tang","raw_affiliation_strings":["State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-9071-137X","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102722683","display_name":"Yuting Cui","orcid":"https://orcid.org/0000-0001-8558-1446"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuting Cui","raw_affiliation_strings":["State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0001-8558-1446","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033730774","display_name":"Linbin Zhang","orcid":"https://orcid.org/0000-0003-1241-5548"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linbin Zhang","raw_affiliation_strings":["State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0003-1241-5548","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082887216","display_name":"Gangyao Kuang","orcid":"https://orcid.org/0000-0003-2620-889X"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Gangyao Kuang","raw_affiliation_strings":["State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":14.2987,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.981923,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"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/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"}},{"id":"https://openalex.org/T12740","display_name":"Gait Recognition and Analysis","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.7968959808349609},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7884036302566528},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.780978798866272},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.761171817779541},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6904469728469849},{"id":"https://openalex.org/keywords/automatic-target-recognition","display_name":"Automatic target recognition","score":0.6515716314315796},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6511488556861877},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5288432836532593},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5213928818702698},{"id":"https://openalex.org/keywords/cross-entropy","display_name":"Cross entropy","score":0.48321521282196045},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4410536587238312},{"id":"https://openalex.org/keywords/inverse-synthetic-aperture-radar","display_name":"Inverse synthetic aperture radar","score":0.4363342225551605},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.39379042387008667},{"id":"https://openalex.org/keywords/radar-imaging","display_name":"Radar imaging","score":0.37986284494400024},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36555910110473633},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.30215275287628174}],"concepts":[{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.7968959808349609},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7884036302566528},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.780978798866272},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.761171817779541},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6904469728469849},{"id":"https://openalex.org/C117623542","wikidata":"https://www.wikidata.org/wiki/Q621974","display_name":"Automatic target recognition","level":3,"score":0.6515716314315796},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6511488556861877},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5288432836532593},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5213928818702698},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.48321521282196045},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4410536587238312},{"id":"https://openalex.org/C109094680","wikidata":"https://www.wikidata.org/wiki/Q6060432","display_name":"Inverse synthetic aperture radar","level":4,"score":0.4363342225551605},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39379042387008667},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.37986284494400024},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36555910110473633},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.30215275287628174},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2021.3109342","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2021.3109342","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":[],"awards":[{"id":"https://openalex.org/G8306244626","display_name":null,"funder_award_id":"62001480","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1571809481","https://openalex.org/W1857884451","https://openalex.org/W1966040997","https://openalex.org/W2095705004","https://openalex.org/W2171590421","https://openalex.org/W2214352687","https://openalex.org/W2292481059","https://openalex.org/W2410591237","https://openalex.org/W2520774990","https://openalex.org/W2601450892","https://openalex.org/W2908510526","https://openalex.org/W2962949934","https://openalex.org/W2998624838","https://openalex.org/W3012255272","https://openalex.org/W3032972472","https://openalex.org/W3118957360","https://openalex.org/W3121583704","https://openalex.org/W3131937156","https://openalex.org/W3136341174","https://openalex.org/W6674330103","https://openalex.org/W6735236233","https://openalex.org/W6757817989"],"related_works":["https://openalex.org/W2540450177","https://openalex.org/W2170580735","https://openalex.org/W3041793138","https://openalex.org/W1552305638","https://openalex.org/W3013723914","https://openalex.org/W2547107543","https://openalex.org/W2031673444","https://openalex.org/W1994788526","https://openalex.org/W3039343861","https://openalex.org/W157990886"],"abstract_inverted_index":{"Studying":[0],"synthetic":[1,160],"aperture":[2,161],"radar":[3,162],"automatic":[4],"target":[5,134,165],"recognition":[6,137,166],"(SAR-ATR)":[7],"under":[8],"small":[9,34,168],"samples":[10,35,169],"can":[11],"get":[12],"rid":[13],"of":[14,22,47,97,127],"the":[15,20,23,28,44,67,72,79,93,112,115,124,142,149,156],"sample":[16],"dependence":[17],"and":[18,71,95,122,132,136,153],"improve":[19],"practicality":[21],"deep":[24,29],"learning":[25,30],"model.":[26],"However,":[27],"model":[31,116],"trained":[32],"with":[33],"is":[36,59,105,146],"prone":[37],"to":[38,42,84,155],"overfitting.":[39],"In":[40],"order":[41],"solve":[43],"above":[45],"problem":[46],"SAR-ATR,":[48],"a":[49,102],"novel":[50],"loss":[51,56,69,74,144,151],"function":[52,57,70,145,152],"called":[53],"limited":[54],"data":[55],"(LDLF)":[58],"proposed":[60,143],"in":[61,159],"this":[62],"letter,":[63],"which":[64],"organically":[65],"combines":[66],"cross-entropy":[68,150],"contrastive":[73],"function.":[75],"The":[76],"LDLF":[77],"supervises":[78],"convolutional":[80],"neural":[81],"network":[82],"(CNN)":[83],"learn":[85],"strong":[86],"generalization":[87],"performance":[88],"features.":[89],"Then,":[90],"for":[91,111],"simplifying":[92],"training":[94],"testing":[96,125],"CNN":[98],"based":[99],"on":[100,130],"LDLF,":[101],"feature-combined":[103],"module":[104,108],"proposed.":[106],"This":[107],"makes":[109],"up":[110],"limitation":[113],"that":[114,141],"input":[117],"must":[118],"be":[119],"image":[120,164],"pairs":[121],"simplifies":[123],"process":[126],"CNN.":[128],"Experiments":[129],"moving":[131],"stationary":[133],"acquisition":[135],"(MSTAR)":[138],"datasets":[139],"show":[140],"better":[147],"than":[148],"superior":[154],"existing":[157],"methods":[158],"(SAR)":[163],"using":[167],"data.":[170]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}