{"id":"https://openalex.org/W3008920213","doi":"https://doi.org/10.1109/jstars.2020.2974577","title":"Semisupervised Variational Generative Adversarial Networks for Hyperspectral Image Classification","display_name":"Semisupervised Variational Generative Adversarial Networks for Hyperspectral Image Classification","publication_year":2020,"publication_date":"2020-01-01","ids":{"openalex":"https://openalex.org/W3008920213","doi":"https://doi.org/10.1109/jstars.2020.2974577","mag":"3008920213"},"language":"en","primary_location":{"id":"doi:10.1109/jstars.2020.2974577","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2020.2974577","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/8994817/09007673.pdf","source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/4609443/8994817/09007673.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073640524","display_name":"Chao Tao","orcid":"https://orcid.org/0000-0003-0071-310X"},"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":"Chao Tao","raw_affiliation_strings":["School of Geosciences and InfoPhysics, Central South University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0003-0071-310X","affiliations":[{"raw_affiliation_string":"School of Geosciences and InfoPhysics, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100743778","display_name":"Hao Wang","orcid":"https://orcid.org/0000-0002-3964-479X"},"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":"Hao Wang","raw_affiliation_strings":["School of Geosciences and InfoPhysics, Central South University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-3964-479X","affiliations":[{"raw_affiliation_string":"School of Geosciences and InfoPhysics, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002710782","display_name":"Ji Qi","orcid":"https://orcid.org/0000-0001-7948-579X"},"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":"Ji Qi","raw_affiliation_strings":["School of Geosciences and InfoPhysics, Central South University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0001-7948-579X","affiliations":[{"raw_affiliation_string":"School of Geosciences and InfoPhysics, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100398353","display_name":"Haifeng Li","orcid":"https://orcid.org/0000-0003-1173-6593"},"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":"Haifeng Li","raw_affiliation_strings":["School of Geosciences and InfoPhysics, Central South University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0003-1173-6593","affiliations":[{"raw_affiliation_string":"School of Geosciences and InfoPhysics, Central South University, Changsha, China","institution_ids":["https://openalex.org/I139660479"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1250,"currency":"USD","value_usd":1250},"apc_paid":{"value":1250,"currency":"USD","value_usd":1250},"fwci":2.678,"has_fulltext":true,"cited_by_count":21,"citation_normalized_percentile":{"value":0.916249,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"13","issue":null,"first_page":"914","last_page":"927"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9918000102043152,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7544414401054382},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7230792045593262},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6609793305397034},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6355171799659729},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5896070003509521},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5524991750717163},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5278398990631104},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.46218690276145935},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.4583510756492615},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.45653587579727173},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4250994026660919},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4206293523311615},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4204446077346802}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7544414401054382},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7230792045593262},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6609793305397034},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6355171799659729},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5896070003509521},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5524991750717163},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5278398990631104},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.46218690276145935},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.4583510756492615},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.45653587579727173},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4250994026660919},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4206293523311615},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4204446077346802},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jstars.2020.2974577","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2020.2974577","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/8994817/09007673.pdf","source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:21de79f78dc947549a44526123e7d425","is_oa":true,"landing_page_url":"https://doaj.org/article/21de79f78dc947549a44526123e7d425","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 914-927 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/jstars.2020.2974577","is_oa":true,"landing_page_url":"https://doi.org/10.1109/jstars.2020.2974577","pdf_url":"https://ieeexplore.ieee.org/ielx7/4609443/8994817/09007673.pdf","source":{"id":"https://openalex.org/S117727964","display_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","issn_l":"1939-1404","issn":["1939-1404","2151-1535"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1053941327","display_name":null,"funder_award_id":"41871364","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2090819383","display_name":"\u9ad8\u5206\u8fa8\u7387\u9065\u611f\u5f71\u50cf\"\u573a\u666f-\u76ee\u6807\"\u534f\u540c\u7406\u89e3\u7406\u8bba\u4e0e\u65b9\u6cd5\u7814\u7a76","funder_award_id":"41771458","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2369898081","display_name":null,"funder_award_id":"2017JJ3378","funder_id":"https://openalex.org/F4320322843","funder_display_name":"Natural Science Foundation of\u00a0Hunan Province"},{"id":"https://openalex.org/G4284791750","display_name":null,"funder_award_id":"41301453","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/F4320321514","display_name":"Central South University","ror":"https://ror.org/00f1zfq44"},{"id":"https://openalex.org/F4320322843","display_name":"Natural Science Foundation of\u00a0Hunan Province","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3008920213.pdf","grobid_xml":"https://content.openalex.org/works/W3008920213.grobid-xml"},"referenced_works_count":59,"referenced_works":["https://openalex.org/W284754667","https://openalex.org/W1521436688","https://openalex.org/W1959608418","https://openalex.org/W1969816925","https://openalex.org/W1986658236","https://openalex.org/W1991576946","https://openalex.org/W1997565609","https://openalex.org/W2009286595","https://openalex.org/W2010478697","https://openalex.org/W2029316659","https://openalex.org/W2030476695","https://openalex.org/W2039409148","https://openalex.org/W2049667262","https://openalex.org/W2057463961","https://openalex.org/W2057522815","https://openalex.org/W2092745549","https://openalex.org/W2099471712","https://openalex.org/W2101711129","https://openalex.org/W2113242816","https://openalex.org/W2125389028","https://openalex.org/W2136251662","https://openalex.org/W2153409933","https://openalex.org/W2153747028","https://openalex.org/W2288276382","https://openalex.org/W2522078899","https://openalex.org/W2530816535","https://openalex.org/W2548275288","https://openalex.org/W2548791488","https://openalex.org/W2558391528","https://openalex.org/W2574404198","https://openalex.org/W2593729559","https://openalex.org/W2619503996","https://openalex.org/W2765583965","https://openalex.org/W2766521763","https://openalex.org/W2777427437","https://openalex.org/W2791006446","https://openalex.org/W2792365373","https://openalex.org/W2793964374","https://openalex.org/W2921243146","https://openalex.org/W2921390441","https://openalex.org/W2944413439","https://openalex.org/W2950776302","https://openalex.org/W2951970475","https://openalex.org/W2952745707","https://openalex.org/W2963373786","https://openalex.org/W2963426391","https://openalex.org/W4214564766","https://openalex.org/W4241152325","https://openalex.org/W4297800839","https://openalex.org/W4320013936","https://openalex.org/W6610178563","https://openalex.org/W6640963894","https://openalex.org/W6678815747","https://openalex.org/W6692550842","https://openalex.org/W6718379498","https://openalex.org/W6729482032","https://openalex.org/W6734209382","https://openalex.org/W6738546315","https://openalex.org/W6764051988"],"related_works":["https://openalex.org/W4365211920","https://openalex.org/W3014948380","https://openalex.org/W4391584540","https://openalex.org/W2888032422","https://openalex.org/W4380551139","https://openalex.org/W4317695495","https://openalex.org/W4395044357","https://openalex.org/W2996316059","https://openalex.org/W4287117424","https://openalex.org/W4387506531"],"abstract_inverted_index":{"Although":[0],"the":[1,14,49,54,68,109,113,130,142,157,162,169,193],"hyperspectral":[2],"image":[3],"(HSI)":[4],"classification":[5,114,153,181],"is":[6,19,65],"extensively":[7],"investigated,":[8],"this":[9,23,27,77],"task":[10],"remains":[11],"challenging":[12],"when":[13,192],"number":[15,98],"of":[16,51,99,149],"labeled":[17,88,194],"samples":[18,32,89,92,125],"extremely":[20],"limited.":[21,197],"In":[22,41],"article,":[24],"we":[25,103],"overcome":[26],"challenge":[28],"by":[29],"using":[30,71,86,161],"synthetic":[31],"and":[33,112,175],"proposing":[34],"semisupervised":[35,69,189],"variational":[36],"generative":[37,81,184],"adversarial":[38],"networks":[39],"(GANs).":[40],"contrast":[42],"to":[43,67,129],"conditional":[44],"GAN":[45],"(previously":[46],"used":[47],"for":[48,179],"generation":[50,110],"HSI":[52,138,180],"samples),":[53],"proposed":[55,143,170],"approach":[56],"has":[57],"two":[58],"novel":[59],"aspects.":[60],"First,":[61],"an":[62,72,147],"encoder-decoder":[63],"network":[64,111],"extended":[66],"context":[70],"ensemble":[73],"prediction":[74],"technique.":[75],"Through":[76],"technique,":[78],"our":[79,120],"deep":[80],"model":[82,121,144,159,171],"can":[83,122,127,145,172],"be":[84],"trained":[85],"limited":[87],"(only":[90],"five":[91],"per":[93],"class)":[94],"with":[95,156],"a":[96,105],"large":[97],"unlabeled":[100],"samples.":[101],"Second,":[102],"build":[104],"collaborative":[106],"relationship":[107],"between":[108],"network.":[115],"This":[116],"property":[117],"enables":[118],"that":[119,126,141,168],"produce":[123],"meaningful":[124],"contribute":[128],"final":[131],"classification.":[132],"The":[133],"experiments":[134],"on":[135],"four":[136],"benchmark":[137],"datasets":[139],"demonstrate":[140],"achieve":[146,173],"increase":[148],">10%":[150],"in":[151],"overall":[152],"accuracy":[154],"compared":[155],"baseline":[158],"without":[160],"generated":[163],"sample.":[164],"We":[165],"also":[166],"show":[167],"better":[174],"more":[176],"robust":[177],"performance":[178],"than":[182],"other":[183],"methods":[185],"as":[186,188],"well":[187],"methods,":[190],"especially":[191],"data":[195],"are":[196]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
