{"id":"https://openalex.org/W4404035531","doi":"https://doi.org/10.1109/tgrs.2024.3490537","title":"Few-Shot Hyperspectral Image Classification Using Relational Generative Adversarial Network","display_name":"Few-Shot Hyperspectral Image Classification Using Relational Generative Adversarial Network","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4404035531","doi":"https://doi.org/10.1109/tgrs.2024.3490537"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2024.3490537","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3490537","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Geoscience and Remote Sensing","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/A5100323842","display_name":"Wei Wei","orcid":"https://orcid.org/0000-0003-4488-0102"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wei Wei","raw_affiliation_strings":["College of Computer Science, Beijing University of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101564188","display_name":"Lei Tong","orcid":"https://orcid.org/0000-0002-8904-3810"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Tong","raw_affiliation_strings":["College of Computer Science, Beijing University of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006768038","display_name":"Baoqing Guo","orcid":"https://orcid.org/0000-0002-0110-8107"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baoqing Guo","raw_affiliation_strings":["State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100781212","display_name":"Jun Zhou","orcid":"https://orcid.org/0000-0001-5822-8233"},"institutions":[{"id":"https://openalex.org/I11701301","display_name":"Griffith University","ror":"https://ror.org/02sc3r913","country_code":"AU","type":"education","lineage":["https://openalex.org/I11701301"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jun Zhou","raw_affiliation_strings":["School of Information and Communication Technology, Griffith University, Nathan, QLD, Australia"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Technology, Griffith University, Nathan, QLD, Australia","institution_ids":["https://openalex.org/I11701301"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075048365","display_name":"Chuangbai Xiao","orcid":"https://orcid.org/0000-0002-4676-2479"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chuangbai Xiao","raw_affiliation_strings":["College of Computer Science, Beijing University of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100323842"],"corresponding_institution_ids":["https://openalex.org/I37796252"],"apc_list":null,"apc_paid":null,"fwci":3.6734,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.93835091,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"62","issue":null,"first_page":"1","last_page":"16"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9591000080108643,"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":0.9591000080108643,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9437000155448914,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.919700026512146,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.78370600938797},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.7722960710525513},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6768806576728821},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6676459908485413},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5847605466842651},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5827243328094482},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.5082206726074219},{"id":"https://openalex.org/keywords/one-shot","display_name":"One shot","score":0.48743966221809387},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.47950512170791626},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.43568697571754456},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3301733136177063},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3239611089229584},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.1418779492378235}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.78370600938797},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.7722960710525513},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6768806576728821},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6676459908485413},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5847605466842651},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5827243328094482},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.5082206726074219},{"id":"https://openalex.org/C2992734406","wikidata":"https://www.wikidata.org/wiki/Q413267","display_name":"One shot","level":2,"score":0.48743966221809387},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.47950512170791626},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.43568697571754456},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3301733136177063},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3239611089229584},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.1418779492378235},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2024.3490537","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3490537","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5296790834","display_name":null,"funder_award_id":"RAO2024K03","funder_id":"https://openalex.org/F4320329246","funder_display_name":"State Key Laboratory of Advanced Metallurgy"}],"funders":[{"id":"https://openalex.org/F4320329246","display_name":"State Key Laboratory of Advanced Metallurgy","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":62,"referenced_works":["https://openalex.org/W1508948699","https://openalex.org/W2058795991","https://openalex.org/W2101711129","https://openalex.org/W2105386417","https://openalex.org/W2131725398","https://openalex.org/W2136251662","https://openalex.org/W2140340527","https://openalex.org/W2158400785","https://openalex.org/W2171171329","https://openalex.org/W2500751094","https://openalex.org/W2582369608","https://openalex.org/W2614326984","https://openalex.org/W2743255627","https://openalex.org/W2791006446","https://openalex.org/W2808098982","https://openalex.org/W2890306271","https://openalex.org/W2898204262","https://openalex.org/W2904698365","https://openalex.org/W2912371366","https://openalex.org/W2921445432","https://openalex.org/W2943270518","https://openalex.org/W2944653015","https://openalex.org/W2963943197","https://openalex.org/W2964105864","https://openalex.org/W2970175774","https://openalex.org/W2986799142","https://openalex.org/W2992919850","https://openalex.org/W3007444020","https://openalex.org/W3008789903","https://openalex.org/W3010700973","https://openalex.org/W3028306149","https://openalex.org/W3044522863","https://openalex.org/W3049655825","https://openalex.org/W3096675569","https://openalex.org/W3096831136","https://openalex.org/W3105357426","https://openalex.org/W3132867842","https://openalex.org/W3135396862","https://openalex.org/W3165607318","https://openalex.org/W3193349414","https://openalex.org/W3193619850","https://openalex.org/W3197028786","https://openalex.org/W3205965083","https://openalex.org/W4205421373","https://openalex.org/W4210541032","https://openalex.org/W4226043457","https://openalex.org/W4226070402","https://openalex.org/W4294643831","https://openalex.org/W4367359538","https://openalex.org/W4383752953","https://openalex.org/W4388157208","https://openalex.org/W4400905696","https://openalex.org/W4401880010","https://openalex.org/W4402674148","https://openalex.org/W4403210136","https://openalex.org/W4403747286","https://openalex.org/W6687506355","https://openalex.org/W6717697761","https://openalex.org/W6735913928","https://openalex.org/W6745560452","https://openalex.org/W6748555532","https://openalex.org/W6873519621"],"related_works":["https://openalex.org/W2888032422","https://openalex.org/W2996316059","https://openalex.org/W4385421777","https://openalex.org/W4377980832","https://openalex.org/W2897769091","https://openalex.org/W2845413374","https://openalex.org/W3005996785","https://openalex.org/W4297411772","https://openalex.org/W4235873501","https://openalex.org/W4401590935"],"abstract_inverted_index":{"Hyperspectral":[0],"image":[1],"(HSI)":[2],"classification":[3,77],"is":[4,14,174],"an":[5,123],"essential":[6],"task":[7],"in":[8,45],"remote":[9],"sensing,":[10],"but":[11],"its":[12],"performance":[13],"greatly":[15],"affected":[16],"by":[17],"limited":[18,39],"labeled":[19,40],"samples.":[20,105,166],"Currently,":[21],"generative":[22],"adversarial":[23,124],"network":[24],"(GAN)-based":[25],"methods":[26,60,218],"can":[27,178,194],"generate":[28],"the":[29,34,88,100,115,212],"virtue":[30],"samples":[31,90,130,181],"to":[32,64,86,98,118,182],"augment":[33,87],"training":[35,89],"set.":[36],"However,":[37],"with":[38],"data,":[41],"GANs":[42,85],"perform":[43],"poorly":[44],"capturing":[46],"features":[47],"during":[48],"sample":[49,146],"generation.":[50],"Very":[51],"few":[52],"relation":[53,95,154,159],"networks":[54],"(RNs)":[55],"and":[56,120,137,152,164,191,219,227,233],"few-shot":[57,75],"learning":[58],"(FSL)":[59],"considered":[61],"data":[62,111,117,135],"augmentation":[63],"enhance":[65],"performance.":[66],"To":[67],"address":[68],"this":[69],"challenge,":[70],"we":[71],"propose":[72],"FSHyperRGAN,":[73],"a":[74,110,134,153,169],"HSI":[76,116,208],"method":[78,214],"based":[79],"on":[80,132,205,229],"relational":[81,170],"GAN,":[82],"which":[83,177,193],"uses":[84],"for":[91,144,197],"RNs,":[92],"while":[93,148],"leveraging":[94],"feature":[96,171],"extraction":[97],"guide":[99,183],"generation":[101,125],"of":[102,223],"specific":[103],"class":[104],"FSHyperRGAN":[106,186],"comprises":[107],"four":[108],"modules:":[109],"processing":[112],"module":[113,127,140,157],"converting":[114],"1-D":[119,198],"3-D":[121,201],"features,":[122],"(AG)":[126],"synthesizing":[128],"virtual":[129,180],"conditioned":[131],"labels,":[133],"embedding":[136],"reconstruction":[138,147],"(DER)":[139],"encoding":[141],"latent":[142],"spaces":[143],"accurate":[145],"preserving":[149],"category":[150],"characteristics,":[151],"computation":[155],"(RC)":[156],"computing":[158],"scores":[160],"across":[161],"generated,":[162],"reconstructed,":[163],"original":[165],"In":[167],"addition,":[168],"matching":[172],"scheme":[173],"also":[175],"applied,":[176],"use":[179],"classification.":[184,203],"Two":[185],"frameworks":[187],"are":[188],"designed,":[189],"1D-FSHyperRGAN":[190],"3D-FSHyperRGAN,":[192],"be":[195],"utilized":[196],"spectral":[199],"or":[200],"spatial-spectral":[202],"Experiments":[204],"widely":[206],"used":[207],"datasets":[209],"illustrate":[210],"that":[211],"proposed":[213],"outperforms":[215],"several":[216],"state-of-the-art":[217],"achieves":[220],"overall":[221],"accuracies":[222],"92.64%,":[224],"86.82%,":[225],"83.64%,":[226],"84.57%":[228],"KSC,":[230],"PaviaU,":[231],"Houston,":[232],"WHU-Hi-HongHu":[234],"datasets,":[235],"respectively.":[236]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":8}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}