{"id":"https://openalex.org/W2883279772","doi":"https://doi.org/10.1109/tgrs.2018.2849967","title":"SAR ATR of Ground Vehicles Based on LM-BN-CNN","display_name":"SAR ATR of Ground Vehicles Based on LM-BN-CNN","publication_year":2018,"publication_date":"2018-07-26","ids":{"openalex":"https://openalex.org/W2883279772","doi":"https://doi.org/10.1109/tgrs.2018.2849967","mag":"2883279772"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2018.2849967","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2018.2849967","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","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/A5080509511","display_name":"Feng Zhou","orcid":"https://orcid.org/0000-0002-1514-7393"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Feng Zhou","raw_affiliation_strings":["Key Laboratory of Electronic Information Countermeasure and Simulation of the Education Ministry of China, Xidian University, Xi\u2019an, China","Key Laboratory of Electronic Information Countermeasure and Simulation of the Education Ministry of China, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Electronic Information Countermeasure and Simulation of the Education Ministry of China, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Key Laboratory of Electronic Information Countermeasure and Simulation of the Education Ministry of China, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100336148","display_name":"Li Wang","orcid":"https://orcid.org/0000-0003-0040-5561"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Wang","raw_affiliation_strings":["Key Laboratory of Electronic Information Countermeasure and Simulation of the Education Ministry of China, Xidian University, Xi\u2019an, China","Key Laboratory of Electronic Information Countermeasure and Simulation of the Education Ministry of China, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Electronic Information Countermeasure and Simulation of the Education Ministry of China, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Key Laboratory of Electronic Information Countermeasure and Simulation of the Education Ministry of China, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076901636","display_name":"Xueru Bai","orcid":"https://orcid.org/0000-0001-9283-1810"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueru Bai","raw_affiliation_strings":["National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an, China","National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032676248","display_name":"Hui Ye","orcid":"https://orcid.org/0000-0003-0193-780X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ye Hui","raw_affiliation_strings":["National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an, China","National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5080509511"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":95.0512,"has_fulltext":false,"cited_by_count":108,"citation_normalized_percentile":{"value":0.99899288,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"56","issue":"12","first_page":"7282","last_page":"7293"},"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":0.9987000226974487,"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":0.9940999746322632,"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":0.8535754084587097},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.8180060386657715},{"id":"https://openalex.org/keywords/clutter","display_name":"Clutter","score":0.7642239928245544},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7584514021873474},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7490381002426147},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.741255521774292},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.680171012878418},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6521048545837402},{"id":"https://openalex.org/keywords/constant-false-alarm-rate","display_name":"Constant false alarm rate","score":0.5559254288673401},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.5102088451385498},{"id":"https://openalex.org/keywords/target-acquisition","display_name":"Target acquisition","score":0.48551106452941895},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4716334044933319},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4489993155002594},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.3959997296333313},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3800659775733948},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3176073431968689}],"concepts":[{"id":"https://openalex.org/C117623542","wikidata":"https://www.wikidata.org/wiki/Q621974","display_name":"Automatic target recognition","level":3,"score":0.8535754084587097},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.8180060386657715},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.7642239928245544},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7584514021873474},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7490381002426147},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.741255521774292},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.680171012878418},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6521048545837402},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.5559254288673401},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.5102088451385498},{"id":"https://openalex.org/C2779726219","wikidata":"https://www.wikidata.org/wiki/Q7685884","display_name":"Target acquisition","level":2,"score":0.48551106452941895},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4716334044933319},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4489993155002594},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.3959997296333313},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3800659775733948},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3176073431968689},{"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/tgrs.2018.2849967","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2018.2849967","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.4399999976158142}],"awards":[{"id":"https://openalex.org/G3007315598","display_name":null,"funder_award_id":"61522114","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7996373070","display_name":null,"funder_award_id":"61631019","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":55,"referenced_works":["https://openalex.org/W253795887","https://openalex.org/W322998299","https://openalex.org/W639708223","https://openalex.org/W1521679764","https://openalex.org/W1571809481","https://openalex.org/W1784665405","https://openalex.org/W1836465849","https://openalex.org/W1885185971","https://openalex.org/W1958168828","https://openalex.org/W1983364832","https://openalex.org/W2022508996","https://openalex.org/W2038501427","https://openalex.org/W2038591350","https://openalex.org/W2045526238","https://openalex.org/W2065337811","https://openalex.org/W2083319540","https://openalex.org/W2086031864","https://openalex.org/W2095705004","https://openalex.org/W2111525901","https://openalex.org/W2112796928","https://openalex.org/W2138287042","https://openalex.org/W2142532318","https://openalex.org/W2148791593","https://openalex.org/W2150486153","https://openalex.org/W2154610955","https://openalex.org/W2156387975","https://openalex.org/W2160815625","https://openalex.org/W2163605009","https://openalex.org/W2184045248","https://openalex.org/W2249949503","https://openalex.org/W2269648248","https://openalex.org/W2292481059","https://openalex.org/W2331236142","https://openalex.org/W2356798126","https://openalex.org/W2410591237","https://openalex.org/W2416143270","https://openalex.org/W2544339223","https://openalex.org/W2546781373","https://openalex.org/W2588453093","https://openalex.org/W2615263668","https://openalex.org/W2752788177","https://openalex.org/W2919115771","https://openalex.org/W2949117887","https://openalex.org/W2963656735","https://openalex.org/W4255949318","https://openalex.org/W6609435719","https://openalex.org/W6611190527","https://openalex.org/W6631245529","https://openalex.org/W6641046732","https://openalex.org/W6674330103","https://openalex.org/W6681136200","https://openalex.org/W6682713193","https://openalex.org/W6682889407","https://openalex.org/W6684191040","https://openalex.org/W6735013348"],"related_works":["https://openalex.org/W1965572316","https://openalex.org/W2112220318","https://openalex.org/W3094709941","https://openalex.org/W3014874275","https://openalex.org/W2006284743","https://openalex.org/W2142969332","https://openalex.org/W2005285226","https://openalex.org/W2800230812","https://openalex.org/W3146088491","https://openalex.org/W1974607455"],"abstract_inverted_index":{"In":[0,138],"recent":[1],"studies,":[2],"synthetic":[3],"aperture":[4],"radar":[5],"(SAR)":[6],"automatic":[7],"target":[8,28,96],"recognition":[9,21,31],"(ATR)":[10],"algorithms":[11],"based":[12,99],"on":[13,100,158],"convolutional":[14],"neural":[15],"network":[16],"(CNN)":[17],"have":[18,163],"achieved":[19,84],"high":[20],"rates":[22],"in":[23,40,49,77,85,125],"the":[24,36,41,58,62,69,86,112,122,126,131,151,159,171],"moving":[25],"and":[26,30,43,147,154,181,192],"stationary":[27],"acquisition":[29],"(MSTAR)":[32],"data":[33,45,105,161],"set.":[34],"However,":[35],"correlation":[37],"between":[38],"clutter":[39,70,136],"training":[42],"test":[44],"sets":[46,106],"is":[47],"ignored":[48],"these":[50],"algorithms,":[51],"although":[52],"most":[53],"of":[54,61,68,133,178],"them":[55],"used":[56],"only":[57],"center":[59],"part":[60],"images":[63],"by":[64],"removing":[65],"a":[66,95],"lot":[67],"but":[71],"not":[72],"everything,":[73],"which":[74,120],"may":[75],"result":[76],"better":[78,168],"performance":[79,169],"than":[80,170],"what":[81],"would":[82],"be":[83],"operational":[87],"scenarios.":[88],"To":[89],"tackle":[90],"this":[91,140],"problem,":[92],"we":[93,110],"propose":[94],"segmentation":[97],"method":[98],"morphological":[101],"operations":[102],"to":[103,129,149,185],"generate":[104],"without":[107],"clutter.":[108],"Then,":[109],"design":[111],"large-margin":[113],"softmax":[114],"(LM-softmax)":[115],"batch-normalization":[116],"CNN":[117],"(LM-BN-CNN)":[118],"structure,":[119],"utilizes":[121],"LM-softmax":[123],"classifier":[124],"last":[127],"layer":[128],"increase":[130,150],"separability":[132],"samples":[134],"after":[135],"removal.":[137],"addition,":[139],"structure":[141],"performs":[142],"BN":[143],"with":[144],"constant":[145],"mean":[146],"variance":[148],"convergence":[152],"speed":[153],"reduce":[155],"overfitting.":[156],"Experiments":[157],"MSTAR":[160],"set":[162],"shown":[164],"that":[165],"LM-BN-CNN":[166],"obtains":[167],"available":[172],"CNNs":[173],"designed":[174],"for":[175],"SAR":[176],"ATR":[177],"ground":[179],"vehicles,":[180],"it":[182],"has":[183],"robustness":[184],"large":[186],"depression":[187],"angle":[188],"variation,":[189],"configuration":[190],"variants,":[191],"version":[193],"variants.":[194]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":18},{"year":2024,"cited_by_count":12},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":24},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":2}],"updated_date":"2026-03-31T07:56:22.981413","created_date":"2025-10-10T00:00:00"}