{"id":"https://openalex.org/W4399939114","doi":"https://doi.org/10.1109/tcsvt.2024.3418979","title":"MIGA-Net: Multi-View Image Information Learning Based on Graph Attention Network for SAR Target Recognition","display_name":"MIGA-Net: Multi-View Image Information Learning Based on Graph Attention Network for SAR Target Recognition","publication_year":2024,"publication_date":"2024-06-24","ids":{"openalex":"https://openalex.org/W4399939114","doi":"https://doi.org/10.1109/tcsvt.2024.3418979"},"language":"en","primary_location":{"id":"doi:10.1109/tcsvt.2024.3418979","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2024.3418979","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"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 Circuits and Systems for Video Technology","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/A5086014775","display_name":"R. F. Wang","orcid":"https://orcid.org/0009-0003-1040-9766"},"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":"Ruiqiu Wang","raw_affiliation_strings":["National Key Laboratory of Radar Signal Processing, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0009-0003-1040-9766","affiliations":[{"raw_affiliation_string":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101972883","display_name":"Tao Su","orcid":"https://orcid.org/0000-0003-1529-4198"},"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":"Tao Su","raw_affiliation_strings":["National Key Laboratory of Radar Signal Processing, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0003-1529-4198","affiliations":[{"raw_affiliation_string":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059624581","display_name":"Dan Xu","orcid":"https://orcid.org/0000-0001-8844-5124"},"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":"Dan Xu","raw_affiliation_strings":["Academy of Advanced Interdisciplinary Research, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0001-8844-5124","affiliations":[{"raw_affiliation_string":"Academy of Advanced Interdisciplinary Research, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060268032","display_name":"Jianlai Chen","orcid":"https://orcid.org/0000-0002-8639-9336"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianlai Chen","raw_affiliation_strings":["School of Automation, Central South University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-8639-9336","affiliations":[{"raw_affiliation_string":"School of Automation, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103228896","display_name":"Liang Yuan","orcid":"https://orcid.org/0000-0001-5494-1250"},"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":"Yuan Liang","raw_affiliation_strings":["National Key Laboratory of Radar Signal Processing, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0001-5494-1250","affiliations":[{"raw_affiliation_string":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":3.5947,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.93213431,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"34","issue":"11","first_page":"10779","last_page":"10792"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9714999794960022,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9714999794960022,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7245068550109863},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.579041063785553},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.45825257897377014},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36603474617004395},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35710588097572327},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1665392816066742}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7245068550109863},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.579041063785553},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.45825257897377014},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36603474617004395},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35710588097572327},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1665392816066742}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcsvt.2024.3418979","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2024.3418979","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"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 Circuits and Systems for Video Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2833243638","display_name":null,"funder_award_id":"62271379","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3902436127","display_name":null,"funder_award_id":"ZYTS24126","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G6656855545","display_name":null,"funder_award_id":"2023J05300","funder_id":"https://openalex.org/F4320321878","funder_display_name":"Natural Science Foundation of Fujian Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321878","display_name":"Natural Science Foundation of Fujian Province","ror":null},{"id":"https://openalex.org/F4320323229","display_name":"National Central University","ror":"https://ror.org/00944ve71"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1677182931","https://openalex.org/W2095391196","https://openalex.org/W2106219791","https://openalex.org/W2124648367","https://openalex.org/W2138287042","https://openalex.org/W2148791593","https://openalex.org/W2158672843","https://openalex.org/W2194775991","https://openalex.org/W2292481059","https://openalex.org/W2410591237","https://openalex.org/W2521772843","https://openalex.org/W2549139847","https://openalex.org/W2615263668","https://openalex.org/W2741749842","https://openalex.org/W2773828237","https://openalex.org/W2883279772","https://openalex.org/W2942454403","https://openalex.org/W2963446712","https://openalex.org/W2964191639","https://openalex.org/W2990641522","https://openalex.org/W3017320838","https://openalex.org/W3032972472","https://openalex.org/W3106935756","https://openalex.org/W3127690492","https://openalex.org/W3131740536","https://openalex.org/W3176482836","https://openalex.org/W3192142973","https://openalex.org/W3194531425","https://openalex.org/W3208926440","https://openalex.org/W3215715933","https://openalex.org/W4226220893","https://openalex.org/W4285288059","https://openalex.org/W4285310237","https://openalex.org/W4313137923","https://openalex.org/W4321770538","https://openalex.org/W4324116447","https://openalex.org/W4365420883","https://openalex.org/W4380303514","https://openalex.org/W4389169598","https://openalex.org/W4390908054","https://openalex.org/W4391132239","https://openalex.org/W4392118985","https://openalex.org/W6631190155","https://openalex.org/W6631943919","https://openalex.org/W6674330103","https://openalex.org/W6682713193","https://openalex.org/W6685562342"],"related_works":["https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2772917594","https://openalex.org/W2775347418","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Neural":[0],"networks":[1],"for":[2,28,65,126,135],"synthetic":[3],"aperture":[4],"radar":[5],"(SAR)":[6],"automatic":[7],"target":[8,136,198],"recognition":[9,201],"often":[10],"encounter":[11],"overfitting":[12],"challenges":[13],"owing":[14],"to":[15,55,84,160,173,220],"limited":[16],"training":[17,75],"samples.":[18],"Moreover,":[19],"the":[20,91,113,123,139,142,157,185,189,194,206,210],"azimuth":[21,148,162],"angle":[22,127],"of":[23,90,188,209],"SAR,":[24],"a":[25,43,99,175],"vital":[26],"parameter":[27],"improving":[29],"network":[30,46,117,140],"generalization,":[31],"is":[32],"frequently":[33],"disregarded":[34],"in":[35],"most":[36],"models.":[37],"In":[38],"response,":[39],"we":[40,60,69,97],"propose":[41],"MIGA-Net,":[42],"classification":[44,57,186],"neural":[45],"that":[47],"effectively":[48],"perceives":[49],"azimuthal":[50,63],"information":[51,82,152],"using":[52],"multi-view":[53],"images":[54],"improve":[56],"performance.":[58],"Specifically,":[59],"quantize":[61],"low-dimensional":[62],"values":[64],"sample-limited":[66],"scenarios.":[67],"Then,":[68],"utilize":[70],"encoded":[71],"image":[72,147],"sequences":[73],"as":[74],"data":[76,125],"because":[77],"they":[78],"encompass":[79],"spatial":[80],"context":[81],"compared":[83,219],"individual":[85],"images.":[86],"After":[87],"extracting":[88],"features":[89,108,121,134,149,169],"sequence":[92,107],"samples":[93],"through":[94],"convolutional":[95,158],"layers,":[96],"design":[98],"two-layer":[100],"output":[101],"module.":[102],"One":[103],"layer":[104,131,172],"converts":[105],"these":[106,133],"into":[109],"graph":[110,115,124],"data.":[111],"Then":[112],"dense":[114],"attention":[116],"(GAT)":[118],"extracts":[119],"contextual":[120],"from":[122,170],"estimation.":[128],"Simultaneously,":[129],"another":[130,171],"combines":[132],"classification.":[137],"During":[138],"training,":[141],"GAT":[143],"module":[144],"can":[145],"extract":[146],"with":[150,167],"powerful":[151],"aggregation":[153],"capabilities.":[154],"It":[155],"supervises":[156],"layers":[159],"learn":[161],"features,":[163],"which":[164],"are":[165],"fused":[166],"class":[168],"obtain":[174],"more":[176],"structured":[177],"feature":[178,181],"domain.":[179],"This":[180],"domain":[182],"significantly":[183],"enhances":[184],"performance":[187,208],"network.":[190],"Experiments":[191],"conducted":[192],"on":[193],"moving":[195],"and":[196,200],"stationary":[197],"acquisition":[199],"(MSTAR)":[202],"dataset":[203],"have":[204],"proven":[205],"superior":[207],"proposed":[211],"method,":[212],"achieving":[213],"at":[214],"least":[215],"1%":[216],"higher":[217],"accuracy":[218],"other":[221],"state-of-the-art":[222],"algorithms.":[223]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":14},{"year":2024,"cited_by_count":2}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}
