{"id":"https://openalex.org/W4392903274","doi":"https://doi.org/10.1109/tgrs.2024.3377610","title":"Hyperspectral Image Classification Using Groupwise Separable Convolutional Vision Transformer Network","display_name":"Hyperspectral Image Classification Using Groupwise Separable Convolutional Vision Transformer Network","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4392903274","doi":"https://doi.org/10.1109/tgrs.2024.3377610"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2024.3377610","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3377610","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/A5040967211","display_name":"Zhuoyi Zhao","orcid":"https://orcid.org/0000-0001-7557-8630"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]},{"id":"https://openalex.org/I4210127074","display_name":"Zhongshan Hospital","ror":"https://ror.org/032x22645","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210127074"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuoyi Zhao","raw_affiliation_strings":["Artificial Intelligence and Computer Vision Laboratory, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan, China","Artificial Intelligence and Computer Vision Laboratory, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Artificial Intelligence and Computer Vision Laboratory, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan, China","institution_ids":["https://openalex.org/I4210127074"]},{"raw_affiliation_string":"Artificial Intelligence and Computer Vision Laboratory, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050455072","display_name":"Xiang Xu","orcid":"https://orcid.org/0000-0002-4388-1915"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]},{"id":"https://openalex.org/I4210127074","display_name":"Zhongshan Hospital","ror":"https://ror.org/032x22645","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210127074"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Xu","raw_affiliation_strings":["Artificial Intelligence and Computer Vision Laboratory, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan, China","Artificial Intelligence and Computer Vision Laboratory, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan, China"],"raw_orcid":"https://orcid.org/0000-0002-4388-1915","affiliations":[{"raw_affiliation_string":"Artificial Intelligence and Computer Vision Laboratory, Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan, China","institution_ids":["https://openalex.org/I4210127074"]},{"raw_affiliation_string":"Artificial Intelligence and Computer Vision Laboratory, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067097659","display_name":"Shutao Li","orcid":"https://orcid.org/0000-0002-0585-9848"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shutao Li","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-0585-9848","affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054292278","display_name":"Antonio Plaza","orcid":"https://orcid.org/0000-0002-9613-1659"},"institutions":[{"id":"https://openalex.org/I80606768","display_name":"Universidad de Extremadura","ror":"https://ror.org/0174shg90","country_code":"ES","type":"education","lineage":["https://openalex.org/I80606768"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Antonio Plaza","raw_affiliation_strings":["Department of Technology of Computers and Communications, Hyperspectral Computing Laboratory, Escuela Politécnica, University of Extremadura, Cáceres, Spain"],"raw_orcid":"https://orcid.org/0000-0002-9613-1659","affiliations":[{"raw_affiliation_string":"Department of Technology of Computers and Communications, Hyperspectral Computing Laboratory, Escuela Politécnica, University of Extremadura, Cáceres, Spain","institution_ids":["https://openalex.org/I80606768"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":32.4836,"has_fulltext":false,"cited_by_count":177,"citation_normalized_percentile":{"value":0.99902919,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"62","issue":null,"first_page":"1","last_page":"17"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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":1.0,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9948999881744385,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.984000027179718,"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":0.6941190361976624},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.676466703414917},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.67390376329422},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6711976528167725},{"id":"https://openalex.org/keywords/pointwise","display_name":"Pointwise","score":0.6555257439613342},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.541404128074646},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5341678261756897},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.42955532670021057},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4106016457080841},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16760295629501343}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6941190361976624},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.676466703414917},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.67390376329422},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6711976528167725},{"id":"https://openalex.org/C2777984123","wikidata":"https://www.wikidata.org/wiki/Q9248237","display_name":"Pointwise","level":2,"score":0.6555257439613342},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.541404128074646},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5341678261756897},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.42955532670021057},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4106016457080841},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16760295629501343},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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/tgrs.2024.3377610","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2024.3377610","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/G1865816430","display_name":null,"funder_award_id":"2023A1515010066","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G6316326147","display_name":null,"funder_award_id":"62271130","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6716103886","display_name":null,"funder_award_id":"T2225019","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8352219898","display_name":null,"funder_award_id":"2021A0101180005","funder_id":"https://openalex.org/F4320335376","funder_display_name":"Medical Science and Technology Foundation of Guangdong Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335376","display_name":"Medical Science and Technology Foundation of Guangdong Province","ror":null},{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W2001289295","https://openalex.org/W2067983477","https://openalex.org/W2070576129","https://openalex.org/W2073748771","https://openalex.org/W2104437233","https://openalex.org/W2171171329","https://openalex.org/W2331319323","https://openalex.org/W2761430711","https://openalex.org/W2766579383","https://openalex.org/W2808098982","https://openalex.org/W2886666371","https://openalex.org/W2889861425","https://openalex.org/W2904698365","https://openalex.org/W2914331134","https://openalex.org/W2963366243","https://openalex.org/W3044657819","https://openalex.org/W3049655825","https://openalex.org/W3094484482","https://openalex.org/W3105357426","https://openalex.org/W3138516171","https://openalex.org/W3170347305","https://openalex.org/W3170874841","https://openalex.org/W3187800423","https://openalex.org/W3210696018","https://openalex.org/W3214821343","https://openalex.org/W4210794570","https://openalex.org/W4221096379","https://openalex.org/W4224247055","https://openalex.org/W4281256697","https://openalex.org/W4285203116","https://openalex.org/W4285299034","https://openalex.org/W4286910290","https://openalex.org/W4292825900","https://openalex.org/W4296339430","https://openalex.org/W4297775537","https://openalex.org/W4312349930","https://openalex.org/W4319069095","https://openalex.org/W4319866034","https://openalex.org/W4385245566","https://openalex.org/W4385574769","https://openalex.org/W4394670654","https://openalex.org/W6684191040","https://openalex.org/W6737664043","https://openalex.org/W6788135285","https://openalex.org/W6802648153"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W143502885","https://openalex.org/W2070598848","https://openalex.org/W2404757046","https://openalex.org/W2044184146","https://openalex.org/W4309346246"],"abstract_inverted_index":{"Recently,":[0],"Vision":[1,89],"Transformer":[2,38,63,90,189],"(ViT)-based":[3],"deep":[4],"learning":[5,45],"models":[6],"have":[7],"achieved":[8],"remarkable":[9],"performance":[10,227],"gains":[11],"in":[12,62,127,147,149,187],"hyperspectral":[13,53,128,217],"image":[14],"classification":[15,226],"(HSIC)":[16],"due":[17],"to":[18,21,112,140,180,195],"their":[19],"abilities":[20],"model":[22],"long-range":[23],"dependencies":[24],"and":[25,40,71,107,121,159,200,209],"extract":[26],"global":[27,60,164],"spatial":[28,70,156,165],"features.":[29,73],"However,":[30],"ViT":[31,82],"is":[32,100,110,138,178,243],"built":[33],"with":[34,173,228,235],"a":[35,46,80,93,101,131,169],"stack":[36],"of":[37,44,49,59,68,103,117,191,204],"blocks":[39,190],"faces":[41],"the":[42,56,65,115,142,151,160,182,202],"challenge":[43],"large":[47],"number":[48,116],"parameters":[50],"when":[51],"processing":[52],"data.":[54],"Besides,":[55],"inherent":[57],"modeling":[58],"correlation":[61],"ignores":[64],"effective":[66],"representation":[67],"local":[69,124,155],"spectral":[72],"To":[74],"address":[75],"these":[76],"issues,":[77],"we":[78],"propose":[79],"lightweight":[81],"network":[83],"known":[84],"as":[85,194,233],"Groupwise":[86,94,132,152],"Separable":[87,95,133],"Convolutional":[88],"(GSC-ViT).":[91],"Firstly,":[92],"Convolution":[96],"(GSC)":[97],"module,":[98],"which":[99,150],"combination":[102],"grouped":[104],"pointwise":[105,171],"convolution":[106],"group":[108],"convolution,":[109],"designed":[111],"significantly":[113],"decrease":[114],"convolutional":[118],"kernel":[119],"parameters,":[120],"effectively":[122],"capture":[123],"spectral-spatial":[125],"information":[126],"image.":[129],"Secondly,":[130],"Multi-Head":[134,144],"Self-Attention":[135,145],"(GSSA)":[136],"module":[137],"employed":[139,179],"substitute":[141,181],"conventional":[143],"(MSA)":[146],"ViT,":[148,192],"Self-Attention(GSA)":[153],"provides":[154,163],"feature":[157,166],"extraction,":[158],"Pointwise":[161],"Self-Attention(PWSA)":[162],"extraction.":[167],"Thirdly,":[168],"simple":[170],"layer":[172,186],"enhanced":[174],"skip":[175],"connection":[176],"mechanism":[177],"Multi-Layer":[183],"Perceptron":[184],"(MLP)":[185],"all":[188],"so":[193],"eliminate":[196],"unnecessary":[197],"nonlinear":[198],"transformations":[199],"facilitate":[201],"fusion":[203],"features":[205],"derived":[206],"from":[207],"GSC":[208],"GSSA":[210],"modules.":[211],"Extensive":[212],"experiments":[213],"on":[214],"four":[215],"benchmark":[216],"datasets":[218],"reveal":[219],"that":[220],"our":[221],"GSC-ViT":[222],"can":[223],"achieve":[224],"surprising":[225],"relatively":[229],"few":[230],"training":[231],"samples":[232],"compared":[234],"some":[236],"existing":[237],"HSIC":[238],"approaches.":[239],"The":[240],"source":[241],"code":[242],"available":[244],"at":[245],"https://github.com/flyzzie/TGRS-GSC-VIT.":[246]},"counts_by_year":[{"year":2026,"cited_by_count":31},{"year":2025,"cited_by_count":114},{"year":2024,"cited_by_count":32}],"updated_date":"2026-07-25T09:21:30.201066","created_date":"2025-10-10T00:00:00"}