{"id":"https://openalex.org/W2741749842","doi":"https://doi.org/10.1109/access.2017.2773363","title":"Multi-Aspect-Aware Bidirectional LSTM Networks for Synthetic Aperture Radar Target Recognition","display_name":"Multi-Aspect-Aware Bidirectional LSTM Networks for Synthetic Aperture Radar Target Recognition","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2741749842","doi":"https://doi.org/10.1109/access.2017.2773363","mag":"2741749842"},"language":"en","primary_location":{"id":"doi:10.1109/access.2017.2773363","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2017.2773363","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2017.2773363","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100690860","display_name":"Fan Zhang","orcid":"https://orcid.org/0000-0002-2058-2373"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Zhang","raw_affiliation_strings":["College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-2058-2373","affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100357677","display_name":"Hu Chen","orcid":"https://orcid.org/0000-0001-9300-6572"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen Hu","raw_affiliation_strings":["National Research Center for High-Performance Computing Engineering Technology, Sugon Information Industry Co., Ltd, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National Research Center for High-Performance Computing Engineering Technology, Sugon Information Industry Co., Ltd, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082881761","display_name":"Qiang Yin","orcid":"https://orcid.org/0000-0002-8413-4756"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiang Yin","raw_affiliation_strings":["College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100317994","display_name":"Wei Li","orcid":"https://orcid.org/0000-0001-7015-7335"},"institutions":[{"id":"https://openalex.org/I75390827","display_name":"Beijing University of Chemical Technology","ror":"https://ror.org/00df5yc52","country_code":"CN","type":"education","lineage":["https://openalex.org/I75390827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Li","raw_affiliation_strings":["College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Information Science and Technology, Beijing University of Chemical Technology, Beijing, China","institution_ids":["https://openalex.org/I75390827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015155189","display_name":"Heng-Chao Li","orcid":"https://orcid.org/0000-0002-9735-570X"},"institutions":[{"id":"https://openalex.org/I4800084","display_name":"Southwest Jiaotong University","ror":"https://ror.org/00hn7w693","country_code":"CN","type":"education","lineage":["https://openalex.org/I4800084"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Heng-Chao Li","raw_affiliation_strings":["Sichuan Provincial Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sichuan Provincial Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University, Chengdu, China","institution_ids":["https://openalex.org/I4800084"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112078435","display_name":"Wen Hong","orcid":"https://orcid.org/0000-0002-1025-9812"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210110458","display_name":"Institute of Electronics","ror":"https://ror.org/01z143507","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210110458"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wen Hong","raw_affiliation_strings":["Institute of Electronics, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Electronics, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210110458","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":114.8335,"has_fulltext":false,"cited_by_count":93,"citation_normalized_percentile":{"value":0.99897171,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"5","issue":null,"first_page":"26880","last_page":"26891"},"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/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9987000226974487,"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":0.9973999857902527,"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/computer-science","display_name":"Computer science","score":0.8022590279579163},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.753150999546051},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.6597661972045898},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.6556140184402466},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6369831562042236},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5779232382774353},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5377495288848877},{"id":"https://openalex.org/keywords/automatic-target-recognition","display_name":"Automatic target recognition","score":0.5363195538520813},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4378202259540558},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4298780858516693},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4162311255931854}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8022590279579163},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.753150999546051},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.6597661972045898},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.6556140184402466},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6369831562042236},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5779232382774353},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5377495288848877},{"id":"https://openalex.org/C117623542","wikidata":"https://www.wikidata.org/wiki/Q621974","display_name":"Automatic target recognition","level":3,"score":0.5363195538520813},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4378202259540558},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4298780858516693},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4162311255931854},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/access.2017.2773363","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2017.2773363","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1707.09875","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1707.09875","pdf_url":"https://arxiv.org/pdf/1707.09875","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},{"id":"pmh:oai:doaj.org/article:e99c082ca99347afba249253b03b372d","is_oa":true,"landing_page_url":"https://doaj.org/article/e99c082ca99347afba249253b03b372d","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 5, Pp 26880-26891 (2017)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2017.2773363","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2017.2773363","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2537594169","display_name":"\u591a\u89d2\u5ea6SAR\u6210\u50cf\u7406\u8bba\u4e0e\u65b9\u6cd5","funder_award_id":"61431018","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3353119307","display_name":null,"funder_award_id":"61501018","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6501339799","display_name":"\u8868\u793a\u6a21\u578b\u6846\u67b6\u4e0b\u9ad8\u5149\u8c31\u9065\u611f\u5f71\u50cf\u5206\u7c7b\u82e5\u5e72\u6280\u672f\u7814\u7a76","funder_award_id":"61571033","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8171917713","display_name":null,"funder_award_id":"4164093","funder_id":"https://openalex.org/F4320322919","funder_display_name":"Natural Science Foundation of Beijing Municipality"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322919","display_name":"Natural Science Foundation of Beijing Municipality","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W304834817","https://openalex.org/W1521436688","https://openalex.org/W1553469512","https://openalex.org/W1689711448","https://openalex.org/W1920235975","https://openalex.org/W1925297696","https://openalex.org/W1990829950","https://openalex.org/W1998489088","https://openalex.org/W2005708641","https://openalex.org/W2012485971","https://openalex.org/W2021915249","https://openalex.org/W2029316659","https://openalex.org/W2038591350","https://openalex.org/W2056764630","https://openalex.org/W2061804056","https://openalex.org/W2064675550","https://openalex.org/W2065337811","https://openalex.org/W2073222764","https://openalex.org/W2079299474","https://openalex.org/W2079735306","https://openalex.org/W2083319540","https://openalex.org/W2085693037","https://openalex.org/W2099887007","https://openalex.org/W2100495367","https://openalex.org/W2101154521","https://openalex.org/W2105386417","https://openalex.org/W2106749053","https://openalex.org/W2108674912","https://openalex.org/W2111847198","https://openalex.org/W2122152426","https://openalex.org/W2124648367","https://openalex.org/W2136922672","https://openalex.org/W2137782143","https://openalex.org/W2137993841","https://openalex.org/W2147276339","https://openalex.org/W2148791593","https://openalex.org/W2149918662","https://openalex.org/W2149940198","https://openalex.org/W2152057649","https://openalex.org/W2158614875","https://openalex.org/W2160445017","https://openalex.org/W2163352848","https://openalex.org/W2186615578","https://openalex.org/W2213917350","https://openalex.org/W2328263349","https://openalex.org/W2348021008","https://openalex.org/W2410591237","https://openalex.org/W2521772843","https://openalex.org/W2548791488","https://openalex.org/W2559324447","https://openalex.org/W2652751060","https://openalex.org/W2919115771","https://openalex.org/W6610843619","https://openalex.org/W6648175404","https://openalex.org/W6688240090","https://openalex.org/W6701626919","https://openalex.org/W6739509182"],"related_works":["https://openalex.org/W3137365474","https://openalex.org/W2899027234","https://openalex.org/W2886347302","https://openalex.org/W2784759481","https://openalex.org/W1545594509","https://openalex.org/W3038591045","https://openalex.org/W1988723959","https://openalex.org/W2053024573","https://openalex.org/W2997424368","https://openalex.org/W4387802373"],"abstract_inverted_index":{"The":[0],"outstanding":[1],"pattern":[2,122],"recognition":[3,19,65],"performance":[4],"of":[5],"deep":[6,28,190],"learning":[7,29,35,43],"brings":[8],"new":[9],"vitality":[10],"to":[11,82,107,126,149,160],"the":[12,44,50,62,68,87,109,115,136,151,157,168,188],"synthetic":[13],"aperture":[14],"radar":[15],"(SAR)":[16],"automatic":[17],"target":[18,162],"(ATR).":[20],"However,":[21],"there":[22],"is":[23,54,80],"a":[24,76,143],"limitation":[25],"in":[26,61],"current":[27],"based":[30],"ATR":[31],"solution":[32],"that":[33,56,167],"each":[34],"process":[36],"only":[37],"handles":[38],"one":[39],"SAR":[40],"image,":[41],"namely":[42],"static":[45],"scattering":[46,58,97],"information,":[47],"while":[48],"missing":[49],"space-varying":[51,57,96,111],"information.":[52],"It":[53],"obvious":[55],"information":[59,98],"introduced":[60],"multi-aspect":[63,110,152],"joint":[64],"should":[66],"improve":[67],"classification":[69],"accuracy":[70,174],"and":[71,118,181],"robustness.":[72],"In":[73],"this":[74,84],"paper,":[75],"novel":[77],"multi-aspect-aware":[78],"method":[79,170],"proposed":[81,169],"achieve":[83,161,172],"idea":[85],"through":[86],"bidirectional":[88,144],"long":[89],"short-term":[90],"memory":[91],"(LSTM)":[92],"recurrent":[93,146],"neural":[94,147],"networksbased":[95],"learning.":[99],"Specifically,":[100],"we":[101,141],"first":[102],"select":[103],"different":[104],"aspect":[105],"images":[106],"generate":[108],"image":[112],"sequences.":[113],"Then,":[114],"Gabor":[116],"filter":[117],"three-patch":[119],"local":[120],"binary":[121],"are":[123,184],"progressively":[124],"implemented":[125],"extract":[127],"comprehensive":[128],"spatial":[129],"features,":[130],"followed":[131],"by":[132],"dimensionality":[133],"reduction":[134],"with":[135,154],"multi-layer":[137],"perceptron":[138],"network.":[139],"Finally,":[140],"design":[142],"LSTM":[145],"network":[148],"learn":[150],"features":[153],"further":[155],"integrating":[156],"softmax":[158],"classifier":[159],"recognition.":[163,177],"Experimental":[164],"results":[165],"demonstrate":[166],"can":[171],"99.9%":[173],"for":[175],"10-class":[176],"Besides,":[178],"its":[179],"anti-noise":[180],"anti-confusion":[182],"performances":[183],"also":[185],"better":[186],"than":[187],"conventional":[189],"learning-based":[191],"methods.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":14},{"year":2019,"cited_by_count":16},{"year":2018,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2017-08-08T00:00:00"}
