{"id":"https://openalex.org/W2985020998","doi":"https://doi.org/10.1109/igarss.2019.8898369","title":"A Semi-Supervised Method for SAR Target Discrimination Based on Co-Training","display_name":"A Semi-Supervised Method for SAR Target Discrimination Based on Co-Training","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2985020998","doi":"https://doi.org/10.1109/igarss.2019.8898369","mag":"2985020998"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2019.8898369","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8898369","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-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/A5100716826","display_name":"Lan Du","orcid":"https://orcid.org/0000-0001-7326-9666"},"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":"Lan Du","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":"middle","author":{"id":"https://openalex.org/A5100761418","display_name":"Yan Wang","orcid":"https://orcid.org/0000-0001-5341-6040"},"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":"Yan Wang","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/A5045008929","display_name":"Weitong Xie","orcid":null},"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":"Weitong Xie","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":3,"corresponding_author_ids":["https://openalex.org/A5100716826"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":2.3802,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.89743961,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9966999888420105,"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":0.9966999888420105,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9805999994277954,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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":0.9801999926567078,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/co-training","display_name":"Co-training","score":0.8397477865219116},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7500483393669128},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7475461363792419},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7165642976760864},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6909526586532593},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.6213237047195435},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5837403535842896},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.5625560283660889},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4467966556549072},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4351833760738373},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.42490410804748535},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.4228728115558624},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.41571053862571716},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38695597648620605},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.15595018863677979}],"concepts":[{"id":"https://openalex.org/C2776959682","wikidata":"https://www.wikidata.org/wiki/Q17005296","display_name":"Co-training","level":3,"score":0.8397477865219116},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7500483393669128},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7475461363792419},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7165642976760864},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6909526586532593},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.6213237047195435},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5837403535842896},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.5625560283660889},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4467966556549072},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4351833760738373},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.42490410804748535},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.4228728115558624},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.41571053862571716},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38695597648620605},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.15595018863677979},{"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},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2019.8898369","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2019.8898369","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6000000238418579,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1545594509","https://openalex.org/W1824737917","https://openalex.org/W2033801480","https://openalex.org/W2048679005","https://openalex.org/W2079192682","https://openalex.org/W2136504847","https://openalex.org/W2142532318","https://openalex.org/W2517475170","https://openalex.org/W6638609593","https://openalex.org/W6680140577","https://openalex.org/W6681136200"],"related_works":["https://openalex.org/W2133556223","https://openalex.org/W1520691178","https://openalex.org/W2186473728","https://openalex.org/W4312414840","https://openalex.org/W192740413","https://openalex.org/W2131153761","https://openalex.org/W2891078859","https://openalex.org/W60792937","https://openalex.org/W2913865199","https://openalex.org/W2059598258"],"abstract_inverted_index":{"In":[0,28],"synthetic":[1],"aperture":[2],"radar":[3],"(SAR)":[4],"target":[5,36],"discrimination,":[6],"supervised":[7,124],"methods,":[8],"which":[9,42,134],"need":[10],"lots":[11],"of":[12],"labeled":[13,21,45,128],"samples,":[14,129],"are":[15,94],"widely":[16],"used.":[17],"However,":[18],"obtaining":[19],"sufficient":[20],"samples":[22,57],"is":[23,73,104,118],"usually":[24],"costly":[25],"and":[26,46,58],"laborious.":[27],"this":[29],"paper,":[30],"we":[31,50],"propose":[32],"a":[33],"semi-supervised":[34],"SAR":[35],"discrimination":[37],"method":[38,103,117],"based":[39],"on":[40,84],"co-training,":[41],"exploits":[43],"both":[44],"unlabeled":[47],"samples.":[48],"Firstly,":[49],"extract":[51],"Lincoln":[52],"features":[53],"from":[54],"the":[55,70,85,91,98,107,115,123,132],"training":[56],"separate":[59],"them":[60],"into":[61],"two":[62,78,87],"subsets":[63],"according":[64],"to":[65,75,96,122],"their":[66],"physical":[67],"meaning.":[68],"Then,":[69],"co-training":[71],"algorithm":[72],"utilized":[74],"iteratively":[76],"train":[77],"support":[79],"vector":[80],"machine":[81],"(SVM)":[82],"classifiers":[83,93],"extracted":[86],"feature":[88,138],"subsets.":[89],"Finally,":[90],"trained":[92],"exploited":[95],"classify":[97],"test":[99],"data.":[100],"The":[101],"proposed":[102,116],"evaluated":[105],"with":[106,126],"miniSAR":[108],"real":[109],"dataset.":[110],"Experimental":[111],"results":[112],"demonstrates":[113],"that":[114],"not":[119],"only":[120,135],"superior":[121],"methods":[125],"little":[127],"but":[130],"also":[131],"self-learning":[133],"uses":[136],"one":[137],"subset.":[139]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}