{"id":"https://openalex.org/W3092494465","doi":"https://doi.org/10.1109/lgrs.2020.3027363","title":"Target Classification for Single-Channel SAR Images Based on Transfer Learning With Subaperture Decomposition","display_name":"Target Classification for Single-Channel SAR Images Based on Transfer Learning With Subaperture Decomposition","publication_year":2020,"publication_date":"2020-10-08","ids":{"openalex":"https://openalex.org/W3092494465","doi":"https://doi.org/10.1109/lgrs.2020.3027363","mag":"3092494465"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2020.3027363","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3027363","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/A5073837122","display_name":"Zhaocheng Wang","orcid":"https://orcid.org/0000-0003-4890-2540"},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaocheng Wang","raw_affiliation_strings":["Tianjin Key Laboratory of Electronic Materials and Devices, School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0003-4890-2540","affiliations":[{"raw_affiliation_string":"Tianjin Key Laboratory of Electronic Materials and Devices, School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China","institution_ids":["https://openalex.org/I184843921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007927453","display_name":"Xiaoya Fu","orcid":null},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoya Fu","raw_affiliation_strings":["Tianjin Key Laboratory of Electronic Materials and Devices, School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tianjin Key Laboratory of Electronic Materials and Devices, School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China","institution_ids":["https://openalex.org/I184843921"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068081929","display_name":"Kewen Xia","orcid":"https://orcid.org/0000-0003-3968-481X"},"institutions":[{"id":"https://openalex.org/I184843921","display_name":"Hebei University of Technology","ror":"https://ror.org/018hded08","country_code":"CN","type":"education","lineage":["https://openalex.org/I184843921"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kewen Xia","raw_affiliation_strings":["Tianjin Key Laboratory of Electronic Materials and Devices, School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0003-3968-481X","affiliations":[{"raw_affiliation_string":"Tianjin Key Laboratory of Electronic Materials and Devices, School of Electronics and Information Engineering, Hebei University of Technology, Tianjin, China","institution_ids":["https://openalex.org/I184843921"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I184843921"],"apc_list":null,"apc_paid":null,"fwci":17.9531,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.98556705,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"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":0.9998000264167786,"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":0.9998000264167786,"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":0.9961000084877014,"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":0.9894000291824341,"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/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.796700119972229},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7606891393661499},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.746974766254425},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.6748045682907104},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6480879783630371},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6272740364074707},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6213986873626709},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.56488436460495},{"id":"https://openalex.org/keywords/radar-imaging","display_name":"Radar imaging","score":0.48852670192718506},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.47754085063934326},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4612651467323303},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4360671043395996},{"id":"https://openalex.org/keywords/transfer","display_name":"Transfer (computing)","score":0.42881470918655396},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.40825119614601135},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.1646503508090973},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15852797031402588}],"concepts":[{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.796700119972229},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7606891393661499},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.746974766254425},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.6748045682907104},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6480879783630371},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6272740364074707},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6213986873626709},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.56488436460495},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.48852670192718506},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.47754085063934326},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4612651467323303},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4360671043395996},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.42881470918655396},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.40825119614601135},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.1646503508090973},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15852797031402588},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","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},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2020.3027363","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3027363","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/G3404147324","display_name":null,"funder_award_id":"U1813222","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3510186716","display_name":null,"funder_award_id":"18JCYBJC16500","funder_id":"https://openalex.org/F4320323993","funder_display_name":"Natural Science Foundation of Tianjin City"},{"id":"https://openalex.org/G6893761200","display_name":null,"funder_award_id":"62001155","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"},{"id":"https://openalex.org/F4320323993","display_name":"Natural Science Foundation of Tianjin City","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1966040997","https://openalex.org/W1970764305","https://openalex.org/W2089520361","https://openalex.org/W2410591237","https://openalex.org/W2549139847","https://openalex.org/W2889149612","https://openalex.org/W2892123021","https://openalex.org/W2899757317","https://openalex.org/W2902698094","https://openalex.org/W2931068004","https://openalex.org/W2939196249"],"related_works":["https://openalex.org/W2545123933","https://openalex.org/W2585813813","https://openalex.org/W3110962985","https://openalex.org/W2042726296","https://openalex.org/W2081203575","https://openalex.org/W2096748030","https://openalex.org/W3016428515","https://openalex.org/W2160730947","https://openalex.org/W2747205507","https://openalex.org/W2887270943"],"abstract_inverted_index":{"Synthetic":[0],"aperture":[1],"radar":[2],"(SAR)":[3],"images":[4,31,65,74,83,154],"have":[5,32],"limited":[6],"labeled":[7],"samples,":[8],"and":[9,192],"thus,":[10],"it":[11],"is":[12,46,94,103,193],"difficult":[13],"to":[14,50,75,90,124,155,195],"learn":[15],"a":[16,140],"perfect":[17],"convolutional":[18],"neural":[19],"network":[20],"(CNN)":[21],"model":[22],"for":[23,63],"target":[24,199],"classification.":[25],"The":[26,59,165],"commonly":[27],"used":[28],"single-channel":[29,77,100,132],"SAR":[30,64,78,82,101,133,153],"much":[33],"less":[34],"information":[35],"than":[36],"those":[37],"of":[38,55,130,185,189,206],"the":[39,52,56,68,71,76,81,99,117,127,131,148,159,170,177,196,204,207],"three-channel":[40,72],"natural":[41,73,161],"images.":[42],"Transfer":[43],"learning":[44],"(TL)":[45],"an":[47,182],"effective":[48],"way":[49],"improve":[51],"generalization":[53],"ability":[54],"CNN":[57],"model.":[58],"existing":[60],"TL":[61,142,157],"methods":[62],"usually":[66],"transfer":[67],"knowledge":[69],"from":[70,87],"images,":[79,134],"where":[80,147],"are":[84],"simply":[85],"duplicated":[86],"one":[88],"channel":[89],"three":[91,114],"channels.":[92],"This":[93],"obviously":[95],"not":[96],"reasonable.":[97],"Indeed,":[98],"image":[102,162],"complex":[104],"valued,":[105],"which":[106,202],"can":[107,150],"be":[108],"divided":[109],"into":[110],"multiple":[111],"channels":[112],"(e.g.,":[113],"channels)":[115],"via":[116],"subaperture":[118],"decomposition":[119],"(SD)":[120],"algorithm.":[121],"In":[122],"order":[123],"fully":[125],"utilize":[126],"complex-valued":[128],"data":[129,163,173],"in":[135],"this":[136],"letter,":[137],"we":[138],"propose":[139],"novel":[141],"method":[143,180],"with":[144,158],"SD":[145,149],"(TL-SD),":[146],"generate":[151],"pseudocolor":[152],"realize":[156],"large-scale":[160],"sets.":[164],"experimental":[166],"results":[167],"based":[168],"on":[169,187],"MSTAR":[171],"real":[172],"set":[174],"show":[175],"that":[176],"proposed":[178,208],"TL-SD":[179],"achieves":[181],"average":[183],"accuracy":[184],"99.88%":[186],"classification":[188,200],"ten-class":[190],"targets":[191],"superior":[194],"other":[197],"compared":[198],"methods,":[201],"verify":[203],"effectiveness":[205],"method.":[209]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}