{"id":"https://openalex.org/W4285204365","doi":"https://doi.org/10.1109/tgrs.2022.3185640","title":"BS2T: Bottleneck Spatial\u2013Spectral Transformer for Hyperspectral Image Classification","display_name":"BS2T: Bottleneck Spatial\u2013Spectral Transformer for Hyperspectral Image Classification","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4285204365","doi":"https://doi.org/10.1109/tgrs.2022.3185640"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2022.3185640","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3185640","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/A5003667621","display_name":"Ruoxi Song","orcid":"https://orcid.org/0000-0001-7872-5587"},"institutions":[{"id":"https://openalex.org/I153374732","display_name":"Liaoning Normal University","ror":"https://ror.org/04c3cgg32","country_code":"CN","type":"education","lineage":["https://openalex.org/I153374732"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruoxi Song","raw_affiliation_strings":["School of Geography, Liaoning Normal University, Dalian, China","Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7872-5587","affiliations":[{"raw_affiliation_string":"School of Geography, Liaoning Normal University, Dalian, China","institution_ids":["https://openalex.org/I153374732"]},{"raw_affiliation_string":"Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102940895","display_name":"Yining Feng","orcid":"https://orcid.org/0009-0000-2541-9420"},"institutions":[{"id":"https://openalex.org/I153374732","display_name":"Liaoning Normal University","ror":"https://ror.org/04c3cgg32","country_code":"CN","type":"education","lineage":["https://openalex.org/I153374732"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yining Feng","raw_affiliation_strings":["School of Geography, Liaoning Normal University, Dalian, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Geography, Liaoning Normal University, Dalian, China","institution_ids":["https://openalex.org/I153374732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073096655","display_name":"Cheng Wei","orcid":"https://orcid.org/0000-0003-1281-9243"},"institutions":[{"id":"https://openalex.org/I153374732","display_name":"Liaoning Normal University","ror":"https://ror.org/04c3cgg32","country_code":"CN","type":"education","lineage":["https://openalex.org/I153374732"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Cheng","raw_affiliation_strings":["School of Computer and Information Technology, Liaoning Normal University, Dalian, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer and Information Technology, Liaoning Normal University, Dalian, China","institution_ids":["https://openalex.org/I153374732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040843796","display_name":"Zhenhua Mu","orcid":null},"institutions":[{"id":"https://openalex.org/I153374732","display_name":"Liaoning Normal University","ror":"https://ror.org/04c3cgg32","country_code":"CN","type":"education","lineage":["https://openalex.org/I153374732"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenhua Mu","raw_affiliation_strings":["School of Geography, Liaoning Normal University, Dalian, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Geography, Liaoning Normal University, Dalian, China","institution_ids":["https://openalex.org/I153374732"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055787139","display_name":"Xianghai Wang","orcid":"https://orcid.org/0000-0002-7600-9939"},"institutions":[{"id":"https://openalex.org/I153374732","display_name":"Liaoning Normal University","ror":"https://ror.org/04c3cgg32","country_code":"CN","type":"education","lineage":["https://openalex.org/I153374732"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianghai Wang","raw_affiliation_strings":["School of Geography and the School of Computer and Information Technology, Liaoning Normal University, Dalian, China"],"raw_orcid":"https://orcid.org/0000-0002-7600-9939","affiliations":[{"raw_affiliation_string":"School of Geography and the School of Computer and Information Technology, Liaoning Normal University, Dalian, China","institution_ids":["https://openalex.org/I153374732"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.779,"has_fulltext":false,"cited_by_count":84,"citation_normalized_percentile":{"value":0.98219522,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"60","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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9934999942779541,"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":0.9890000224113464,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7637749910354614},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.733970582485199},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6795559525489807},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6655980348587036},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6584059000015259},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6481804251670837},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6408330202102661},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.5032162070274353},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.49442124366760254},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4012450575828552},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2572935223579407}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7637749910354614},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.733970582485199},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6795559525489807},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6655980348587036},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6584059000015259},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6481804251670837},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6408330202102661},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.5032162070274353},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.49442124366760254},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4012450575828552},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2572935223579407},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2022.3185640","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2022.3185640","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/G15543687","display_name":"\u534f\u540c\u591a\u7ef4\u5ea6\u76f8\u5173\u6027\u548c\u6df1\u5c42\u7279\u5f81\u7684\u9ad8\u5149\u8c31\u5f71\u50cf\u53d8\u5316\u68c0\u6d4b\u7406\u8bba\u4e0e\u65b9\u6cd5\u7814\u7a76","funder_award_id":"41971388","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":64,"referenced_works":["https://openalex.org/W117603826","https://openalex.org/W1975435738","https://openalex.org/W2077028485","https://openalex.org/W2136251662","https://openalex.org/W2152057649","https://openalex.org/W2167685974","https://openalex.org/W2548791488","https://openalex.org/W2572303978","https://openalex.org/W2577238056","https://openalex.org/W2614256707","https://openalex.org/W2752782242","https://openalex.org/W2764034829","https://openalex.org/W2764276316","https://openalex.org/W2765739551","https://openalex.org/W2782522152","https://openalex.org/W2789886710","https://openalex.org/W2790275230","https://openalex.org/W2884276099","https://openalex.org/W2893202042","https://openalex.org/W2894165434","https://openalex.org/W2903382683","https://openalex.org/W2923014074","https://openalex.org/W2937675449","https://openalex.org/W2942170965","https://openalex.org/W2942454403","https://openalex.org/W2948157022","https://openalex.org/W2955496698","https://openalex.org/W2963925437","https://openalex.org/W2965373594","https://openalex.org/W2971432438","https://openalex.org/W2989871747","https://openalex.org/W2991616716","https://openalex.org/W3004445196","https://openalex.org/W3016244469","https://openalex.org/W3028306149","https://openalex.org/W3031696400","https://openalex.org/W3035022492","https://openalex.org/W3035356612","https://openalex.org/W3043181422","https://openalex.org/W3047317383","https://openalex.org/W3081753142","https://openalex.org/W3083068801","https://openalex.org/W3083745499","https://openalex.org/W3096609285","https://openalex.org/W3099831940","https://openalex.org/W3103753223","https://openalex.org/W3114720220","https://openalex.org/W3125860323","https://openalex.org/W3127230150","https://openalex.org/W3128776197","https://openalex.org/W3130265010","https://openalex.org/W3133902755","https://openalex.org/W3140885850","https://openalex.org/W3157158693","https://openalex.org/W3172509117","https://openalex.org/W4214493665","https://openalex.org/W4240485910","https://openalex.org/W4288257146","https://openalex.org/W6604796560","https://openalex.org/W6682137061","https://openalex.org/W6739901393","https://openalex.org/W6757107679","https://openalex.org/W6766673545","https://openalex.org/W6781550413"],"related_works":["https://openalex.org/W3173596272","https://openalex.org/W2781623059","https://openalex.org/W3131722669","https://openalex.org/W2912288872","https://openalex.org/W2406522397","https://openalex.org/W2767651786","https://openalex.org/W3020683561","https://openalex.org/W2940977206","https://openalex.org/W2738461075","https://openalex.org/W3156786002"],"abstract_inverted_index":{"Convolutional":[0],"Neural":[1],"Networks":[2],"(CNNs)":[3],"have":[4],"been":[5],"extensively":[6],"applied":[7],"to":[8,29,75,144,164,182,186],"hyperspectral":[9],"(HS)":[10],"image":[11,23,35,83,96,110,202,228],"classification":[12,24,97,203,229,235],"tasks":[13],"and":[14,41,129,157,177,193,209],"achieved":[15],"promising":[16],"performance.":[17],"However,":[18],"for":[19,94,108,213],"CNN":[20,58,208],"based":[21,205],"HS":[22,34,82,95,109,150,170,201,220,227],"methods,":[25],"it":[26],"is":[27,73,116,211],"hard":[28],"depict":[30,76],"the":[31,44,49,54,57,67,77,113,142,146,161,166,174,188,216,233,244,251],"dependencies":[32,80],"among":[33],"pixels":[36,151],"in":[37,64,105,160],"long-range":[38,78,147],"distanced":[39],"positions":[40],"bands.":[42],"Moreover,":[43],"limited":[45],"receptive":[46],"field":[47],"of":[48,56,81,149,153,169,219,250],"convolutional":[50,137],"layers":[51],"extremely":[52],"hinders":[53],"development":[55],"structure.":[59],"To":[60],"tackle":[61],"these":[62],"problems,":[63],"this":[65,135,197],"paper,":[66],"novel":[68],"Bottleneck":[69,103],"Spatial-Spectral":[70],"Transformer":[71,104],"(BS2T)":[72],"proposed":[74,114,234,252],"global":[79],"pixels,":[84],"which":[85],"can":[86,254],"be":[87,255],"regarded":[88],"as":[89],"a":[90,119,123,130,199,238],"feature":[91,111,120,131],"extraction":[92],"module":[93,128],"networks.":[98],"More":[99],"specifically,":[100],"inspired":[101],"by":[102,141],"computer":[106],"vision,":[107],"extraction,":[112],"BS2T":[115,210],"incorporated":[117],"with":[118,243],"contraction":[121],"module,":[122,163],"multi-head":[124,184],"spatial-spectral":[125],"self-attention":[126],"(MHS2A)":[127],"expansion":[132],"module.":[133],"In":[134],"way,":[136],"operations":[138],"are":[139],"replaced":[140],"MHS2A":[143,162],"capture":[145],"dependency":[148],"regardless":[152],"their":[154],"spatial":[155,179],"position":[156],"distance.":[158],"Meanwhile,":[159],"highlight":[165],"spectral":[167,175,194],"features":[168,218],"images,":[171],"we":[172],"introduce":[173],"information":[176,181],"content":[178],"positional":[180,191],"classical":[183],"self-attentions":[185],"make":[187],"attentions":[189],"more":[190],"aware":[192],"aware.":[195],"On":[196],"basis,":[198],"dual-branch":[200],"framework":[204,236,253],"on":[206,224],"3D":[207],"defined":[212],"jointly":[214],"extracting":[215],"local-global":[217],"images.":[221],"Experimental":[222],"results":[223],"three":[225],"public":[226],"datasets":[230],"show":[231],"that":[232],"achieves":[237],"significant":[239],"improvement":[240],"when":[241],"comparing":[242],"state-of-the-art":[245],"methods.":[246],"The":[247],"source":[248],"code":[249],"downloaded":[256],"from":[257],"https://github.com/srxlnnu/BS2T.":[258]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":23},{"year":2024,"cited_by_count":34},{"year":2023,"cited_by_count":22},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
