{"id":"https://openalex.org/W4312652586","doi":"https://doi.org/10.1109/igarss46834.2022.9884179","title":"Polsar Image Classification Via Auxiliary Classifier Generative Adversarial Network","display_name":"Polsar Image Classification Via Auxiliary Classifier Generative Adversarial Network","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4312652586","doi":"https://doi.org/10.1109/igarss46834.2022.9884179"},"language":"en","primary_location":{"id":"doi:10.1109/igarss46834.2022.9884179","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884179","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 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/A5066026113","display_name":"Wen Xie","orcid":"https://orcid.org/0000-0002-8111-8195"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wen Xie","raw_affiliation_strings":["School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications,Xi'an,China,710121"],"affiliations":[{"raw_affiliation_string":"School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications,Xi'an,China,710121","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100719715","display_name":"Xin Yang","orcid":"https://orcid.org/0000-0003-3428-3806"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Yang","raw_affiliation_strings":["School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications,Xi'an,China,710121"],"affiliations":[{"raw_affiliation_string":"School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications,Xi'an,China,710121","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100644562","display_name":"Ruonan Wang","orcid":"https://orcid.org/0000-0002-2866-9609"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruonan Wang","raw_affiliation_strings":["School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications,Xi'an,China,710121"],"affiliations":[{"raw_affiliation_string":"School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications,Xi'an,China,710121","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036534440","display_name":"Feng Zhao","orcid":"https://orcid.org/0000-0002-4343-8841"},"institutions":[{"id":"https://openalex.org/I4210136859","display_name":"Xi\u2019an University of Posts and Telecommunications","ror":"https://ror.org/04jn0td46","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210136859"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Zhao","raw_affiliation_strings":["School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications,Xi'an,China,710121"],"affiliations":[{"raw_affiliation_string":"School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications,Xi'an,China,710121","institution_ids":["https://openalex.org/I4210136859"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5066026113"],"corresponding_institution_ids":["https://openalex.org/I4210136859"],"apc_list":null,"apc_paid":null,"fwci":0.9921,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.76365166,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1205","last_page":"1208"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and 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/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and 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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9983999729156494,"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":0.994700014591217,"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/discriminator","display_name":"Discriminator","score":0.8728135824203491},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.78850257396698},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7607869505882263},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6691462993621826},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6645430326461792},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.609214723110199},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.49560463428497314},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.4594460725784302},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.43142372369766235},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.35437023639678955},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.346693754196167},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.09842929244041443}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.8728135824203491},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.78850257396698},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7607869505882263},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6691462993621826},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6645430326461792},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.609214723110199},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.49560463428497314},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.4594460725784302},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.43142372369766235},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.35437023639678955},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.346693754196167},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.09842929244041443},{"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/igarss46834.2022.9884179","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9884179","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7400000095367432,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G3534406569","display_name":null,"funder_award_id":"61901365,62071379","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":12,"referenced_works":["https://openalex.org/W1421632428","https://openalex.org/W2112796928","https://openalex.org/W2773504760","https://openalex.org/W2950776302","https://openalex.org/W3094316571","https://openalex.org/W3124574862","https://openalex.org/W3173189807","https://openalex.org/W3201393040","https://openalex.org/W3205977649","https://openalex.org/W4200122389","https://openalex.org/W6729482032","https://openalex.org/W6801841149"],"related_works":["https://openalex.org/W3217069185","https://openalex.org/W4308928038","https://openalex.org/W3049340819","https://openalex.org/W4200430540","https://openalex.org/W3141413246","https://openalex.org/W4322709305","https://openalex.org/W2808862658","https://openalex.org/W4391899165","https://openalex.org/W3010213975","https://openalex.org/W2967552310"],"abstract_inverted_index":{"When":[0],"we":[1],"use":[2],"deep":[3],"learning":[4],"to":[5,25,41,50,62,72],"classify":[6],"PolSAR":[7,31,45,121],"images,":[8],"the":[9,16,22,27,34,53,65,73,79,88,96,100,103,112,124],"lack":[10],"of":[11,30,81,99,105,126],"labeled":[12],"samples":[13,29,36,39,68],"will":[14],"affect":[15],"classification":[17,122],"performance.":[18],"This":[19],"paper":[20],"uses":[21],"ACGAN":[23,54,82],"model":[24,55],"expand":[26],"training":[28],"data,":[32],"utilizing":[33],"generated":[35,67],"and":[37],"original":[38,74],"together":[40],"train":[42],"CNN":[43],"for":[44],"image":[46],"classification.":[47],"In":[48],"addition":[49],"random":[51],"noise,":[52],"also":[56,95],"inputs":[57],"additional":[58],"relevant":[59],"guidance":[60],"information":[61],"ensure":[63],"that":[64],"new":[66],"are":[69],"more":[70],"similar":[71],"data.":[75,101],"Compared":[76,117],"with":[77,118],"GAN,":[78],"discriminator":[80],"can":[83],"distinguish":[84],"not":[85],"only":[86],"whether":[87],"data":[89,115],"is":[90,109,130],"real":[91],"or":[92],"not,":[93],"but":[94],"class":[97],"label":[98],"Subsequently,":[102],"validity":[104],"our":[106,127],"proposed":[107,128],"method":[108,129],"verified":[110],"on":[111],"San":[113],"Francisco":[114],"set.":[116],"other":[119],"classical":[120],"methods,":[123],"accuracy":[125],"improved.":[131]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}