{"id":"https://openalex.org/W3038113894","doi":"https://doi.org/10.1109/tgrs.2020.3003264","title":"FEC: A Feature Fusion Framework for SAR Target Recognition Based on Electromagnetic Scattering Features and Deep CNN Features","display_name":"FEC: A Feature Fusion Framework for SAR Target Recognition Based on Electromagnetic Scattering Features and Deep CNN Features","publication_year":2020,"publication_date":"2020-06-26","ids":{"openalex":"https://openalex.org/W3038113894","doi":"https://doi.org/10.1109/tgrs.2020.3003264","mag":"3038113894"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2020.3003264","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2020.3003264","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/A5100371392","display_name":"Jinsong Zhang","orcid":"https://orcid.org/0000-0002-1004-7721"},"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":"Jinsong Zhang","raw_affiliation_strings":["Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi\u2019an, China","National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an, China","Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an, China","National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-1004-7721","affiliations":[{"raw_affiliation_string":"Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an, 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":"middle","author":{"id":"https://openalex.org/A5021408538","display_name":"Mengdao Xing","orcid":"https://orcid.org/0000-0002-4084-0915"},"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":"Mengdao Xing","raw_affiliation_strings":["Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi\u2019an, China","National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an, China","Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an, China","National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-4084-0915","affiliations":[{"raw_affiliation_string":"Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an, 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/A5101545082","display_name":"Yiyuan Xie","orcid":"https://orcid.org/0000-0002-2206-7375"},"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":"Yiyuan Xie","raw_affiliation_strings":["Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi\u2019an, China","National Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an, China","National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China","Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"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"]},{"raw_affiliation_string":"Collaborative Innovation Center of Information Sensing and Understanding, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":123.9953,"has_fulltext":false,"cited_by_count":176,"citation_normalized_percentile":{"value":0.99938986,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"59","issue":"3","first_page":"2174","last_page":"2187"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":1.0,"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":1.0,"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":0.9991000294685364,"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":0.9991000294685364,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7712679505348206},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7709686756134033},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.7634668350219727},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7175840735435486},{"id":"https://openalex.org/keywords/automatic-target-recognition","display_name":"Automatic target recognition","score":0.7171264290809631},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.7044099569320679},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6659154891967773},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6234686374664307},{"id":"https://openalex.org/keywords/target-acquisition","display_name":"Target acquisition","score":0.5308194160461426},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4169621765613556},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39950045943260193}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7712679505348206},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7709686756134033},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.7634668350219727},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7175840735435486},{"id":"https://openalex.org/C117623542","wikidata":"https://www.wikidata.org/wiki/Q621974","display_name":"Automatic target recognition","level":3,"score":0.7171264290809631},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.7044099569320679},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6659154891967773},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6234686374664307},{"id":"https://openalex.org/C2779726219","wikidata":"https://www.wikidata.org/wiki/Q7685884","display_name":"Target acquisition","level":2,"score":0.5308194160461426},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4169621765613556},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39950045943260193},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/tgrs.2020.3003264","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2020.3003264","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":[{"score":0.5,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"},{"score":0.46000000834465027,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G1213887363","display_name":null,"funder_award_id":"U19B2018","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1725003993","display_name":null,"funder_award_id":"61621005","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5505923119","display_name":null,"funder_award_id":"61825105","funder_id":"https://openalex.org/F4320336125","funder_display_name":"National Science Fund for Distinguished Young Scholars"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320336125","display_name":"National Science Fund for Distinguished Young Scholars","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W322998299","https://openalex.org/W1686810756","https://openalex.org/W1973066300","https://openalex.org/W1980867644","https://openalex.org/W1998943499","https://openalex.org/W2027526914","https://openalex.org/W2065337811","https://openalex.org/W2079299474","https://openalex.org/W2098676252","https://openalex.org/W2108598243","https://openalex.org/W2113242816","https://openalex.org/W2120066054","https://openalex.org/W2122111042","https://openalex.org/W2124014399","https://openalex.org/W2124386111","https://openalex.org/W2124648367","https://openalex.org/W2129812935","https://openalex.org/W2132311402","https://openalex.org/W2149918662","https://openalex.org/W2150486153","https://openalex.org/W2153635508","https://openalex.org/W2158672843","https://openalex.org/W2159582914","https://openalex.org/W2163605009","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2246417572","https://openalex.org/W2281856034","https://openalex.org/W2291529341","https://openalex.org/W2292481059","https://openalex.org/W2348120822","https://openalex.org/W2358096008","https://openalex.org/W2387048185","https://openalex.org/W2405680777","https://openalex.org/W2410591237","https://openalex.org/W2466539075","https://openalex.org/W2517475170","https://openalex.org/W2520537062","https://openalex.org/W2521772843","https://openalex.org/W2538244214","https://openalex.org/W2544789064","https://openalex.org/W2555090407","https://openalex.org/W2578577414","https://openalex.org/W2588453093","https://openalex.org/W2615263668","https://openalex.org/W2620429297","https://openalex.org/W2730249686","https://openalex.org/W2735164692","https://openalex.org/W2753007557","https://openalex.org/W2765957155","https://openalex.org/W2782266583","https://openalex.org/W2790279720","https://openalex.org/W2810694441","https://openalex.org/W2883279772","https://openalex.org/W2884561390","https://openalex.org/W2886347302","https://openalex.org/W2889149612","https://openalex.org/W2898947732","https://openalex.org/W2944467232","https://openalex.org/W2964191639","https://openalex.org/W2969675848","https://openalex.org/W2988603195","https://openalex.org/W2998340221","https://openalex.org/W6611190527","https://openalex.org/W6637373629","https://openalex.org/W6684191040","https://openalex.org/W6741295749"],"related_works":["https://openalex.org/W3137365474","https://openalex.org/W2784759481","https://openalex.org/W3038591045","https://openalex.org/W2053024573","https://openalex.org/W2106749053","https://openalex.org/W2773828237","https://openalex.org/W4380446815","https://openalex.org/W2945014618","https://openalex.org/W2045526238","https://openalex.org/W3133339891"],"abstract_inverted_index":{"The":[0,17],"active":[1],"recognition":[2,19,94,190],"of":[3,147],"interesting":[4],"targets":[5],"has":[6],"been":[7],"a":[8,103,145,173],"vital":[9],"issue":[10],"for":[11,67,83,119],"synthetic":[12],"aperture":[13],"radar":[14],"(SAR)":[15],"systems.":[16],"SAR":[18,139],"methods":[20,79],"are":[21],"mainly":[22],"grouped":[23],"as":[24],"follows:":[25],"extracting":[26],"image":[27,33],"features":[28,112,116,182,221],"from":[29,49,136,150,183],"the":[30,36,40,45,50,64,120,124,131,137,158,166,169,193,203,210,213,220,243],"target":[31,51,93],"amplitude":[32,55,184],"or":[34],"matching":[35],"testing":[37],"samples":[38],"with":[39,95,165],"template":[41],"ones":[42],"according":[43],"to":[44,202,223],"scattering":[46,76,110,125,133,152],"centers":[47,134],"extracted":[48,159],"complex":[52,140],"data.":[53],"For":[54,123,168,192],"image-based":[56],"methods,":[57],"convolutional":[58],"neural":[59],"networks":[60],"(CNNs)":[61],"achieve":[62,80,92,188],"nearly":[63],"highest":[65],"accuracy":[66],"images":[68,84,185],"acquired":[69,85],"under":[70,86,98,251],"standard":[71],"operating":[72,88],"conditions":[73,89],"(SOCs),":[74],"while":[75],"center":[77,111,126],"feature-based":[78],"steady":[81],"performance":[82,97],"extended":[87],"(EOCs).":[90],"To":[91],"good":[96],"both":[99,252],"SOCs":[100,253],"and":[101,113,154,215,233,237,249,254],"EOCs,":[102],"feature":[104,163,194,228],"fusion":[105],"framework":[106],"(FEC)":[107],"based":[108],"on":[109,231],"deep":[114],"CNN":[115,214],"is":[117,200],"proposed":[118,244],"first":[121,129],"time.":[122],"features,":[127],"we":[128,143,156,171],"extract":[130,180],"attributed":[132],"(ASCs)":[135],"input":[138],"data,":[141],"then":[142],"construct":[144],"bag":[146],"visual":[148],"words":[149],"these":[151],"centers,":[153],"finally,":[155],"transform":[157],"parameter":[160],"sets":[161],"into":[162],"vectors":[164],"k-means.":[167],"CNN,":[170],"propose":[172],"modified":[174],"VGGNet,":[175],"which":[176,206],"can":[177],"not":[178,207],"only":[179,208],"powerful":[181],"but":[186,217],"also":[187,218],"state-of-the-art":[189],"accuracy.":[191],"fusion,":[195],"discrimination":[196],"correlation":[197,211],"analysis":[198],"(DCA)":[199],"introduced":[201],"FEC":[204,245],"framework,":[205],"maximizes":[209],"between":[212],"ASCs":[216],"decorrelates":[219],"belonging":[222],"different":[224],"categories":[225],"within":[226],"each":[227],"set.":[229],"Experiments":[230],"Moving":[232],"Stationary":[234],"Target":[235],"Acquisition":[236],"Recognition":[238],"(MSTAR)":[239],"database":[240],"demonstrate":[241],"that":[242],"achieves":[246],"superior":[247],"effectiveness":[248],"robustness":[250],"EOCs.":[255]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":41},{"year":2024,"cited_by_count":52},{"year":2023,"cited_by_count":41},{"year":2022,"cited_by_count":24},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}