{"id":"https://openalex.org/W7125897828","doi":"https://doi.org/10.1007/s44443-026-00484-1","title":"Hyperspectral image classification via Manhattan self-attention transformer and adaptive global-local channel attention","display_name":"Hyperspectral image classification via Manhattan self-attention transformer and adaptive global-local channel attention","publication_year":2026,"publication_date":"2026-01-28","ids":{"openalex":"https://openalex.org/W7125897828","doi":"https://doi.org/10.1007/s44443-026-00484-1"},"language":"en","primary_location":{"id":"doi:10.1007/s44443-026-00484-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44443-026-00484-1","pdf_url":null,"source":{"id":"https://openalex.org/S2764955546","display_name":"Journal of King Saud University - Computer and Information Sciences","issn_l":"1319-1578","issn":["1319-1578","2213-1248"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of King Saud University Computer and Information Sciences","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1007/s44443-026-00484-1","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061913887","display_name":"Zhe Meng","orcid":"https://orcid.org/0000-0002-2364-2749"},"institutions":[{"id":"https://openalex.org/I4210088225","display_name":"China National Space Administration","ror":"https://ror.org/004vanv95","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210088225"]},{"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":"Zhe Meng","raw_affiliation_strings":["School of Communications and Information Engineering, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, 710121, China","\u2018Satellite +\u2019 Space AI Natural Resources Intelligent Governance Guangxi University Engineering Research Center, Nanning, China"],"affiliations":[{"raw_affiliation_string":"School of Communications and Information Engineering, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, 710121, China","institution_ids":["https://openalex.org/I4210136859"]},{"raw_affiliation_string":"\u2018Satellite +\u2019 Space AI Natural Resources Intelligent Governance Guangxi University Engineering Research Center, Nanning, China","institution_ids":["https://openalex.org/I4210088225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124118525","display_name":"Pan Yue","orcid":null},"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":"Pan Yue","raw_affiliation_strings":["School of Communications and Information Engineering, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, 710121, China"],"affiliations":[{"raw_affiliation_string":"School of Communications and Information Engineering, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, 710121, China","institution_ids":["https://openalex.org/I4210136859"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100309087","display_name":"Feng Zhao","orcid":"https://orcid.org/0000-0003-0195-3287"},"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\u2019an University of Posts and Telecommunications, Xi\u2019an, 710121, China"],"affiliations":[{"raw_affiliation_string":"School of Communications and Information Engineering, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an, 710121, China","institution_ids":["https://openalex.org/I4210136859"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5061913887"],"corresponding_institution_ids":["https://openalex.org/I4210088225","https://openalex.org/I4210136859"],"apc_list":{"value":1350,"currency":"USD","value_usd":1350},"apc_paid":{"value":1350,"currency":"USD","value_usd":1350},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.19568245,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"38","issue":"3","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9625999927520752,"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":0.9625999927520752,"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":0.006300000008195639,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.004999999888241291,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6093000173568726},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5515000224113464},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.531000018119812},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.47450000047683716},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.4417000114917755},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.3912000060081482},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.39079999923706055},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.37720000743865967}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7135999798774719},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7009000182151794},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6093000173568726},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5515000224113464},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.531000018119812},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.47450000047683716},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.4417000114917755},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39500001072883606},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3912000060081482},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.39079999923706055},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.37720000743865967},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3765000104904175},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.36719998717308044},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3409999907016754},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.33550000190734863},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.32519999146461487},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3240000009536743},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C150060386","wikidata":"https://www.wikidata.org/wiki/Q7574054","display_name":"Spatial correlation","level":2,"score":0.28540000319480896},{"id":"https://openalex.org/C2221639","wikidata":"https://www.wikidata.org/wiki/Q2877","display_name":"Discrete cosine transform","level":3,"score":0.26750001311302185},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.2524999976158142}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s44443-026-00484-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44443-026-00484-1","pdf_url":null,"source":{"id":"https://openalex.org/S2764955546","display_name":"Journal of King Saud University - Computer and Information Sciences","issn_l":"1319-1578","issn":["1319-1578","2213-1248"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of King Saud University Computer and Information Sciences","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s44443-026-00484-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44443-026-00484-1","pdf_url":null,"source":{"id":"https://openalex.org/S2764955546","display_name":"Journal of King Saud University - Computer and Information Sciences","issn_l":"1319-1578","issn":["1319-1578","2213-1248"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of King Saud University Computer and Information Sciences","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W2001289295","https://openalex.org/W2040511445","https://openalex.org/W2519653196","https://openalex.org/W2572303978","https://openalex.org/W2618530766","https://openalex.org/W2752782242","https://openalex.org/W2764276316","https://openalex.org/W2776107444","https://openalex.org/W2787204775","https://openalex.org/W2792332881","https://openalex.org/W2793964374","https://openalex.org/W2808098982","https://openalex.org/W2939573284","https://openalex.org/W2991616716","https://openalex.org/W2994308318","https://openalex.org/W2998142089","https://openalex.org/W3036016333","https://openalex.org/W3049655825","https://openalex.org/W3127230150","https://openalex.org/W3186879605","https://openalex.org/W4200410040","https://openalex.org/W4210794570","https://openalex.org/W4224058197","https://openalex.org/W4280538209","https://openalex.org/W4320339642","https://openalex.org/W4385289414","https://openalex.org/W4393698507","https://openalex.org/W4402726980","https://openalex.org/W4403069257","https://openalex.org/W4403887646","https://openalex.org/W4405056285","https://openalex.org/W4406857990","https://openalex.org/W4408095223"],"related_works":[],"abstract_inverted_index":{"Hyperspectral":[0],"image":[1],"(HSI)":[2],"classification":[3],"is":[4,206,257],"a":[5,70,113,149],"key":[6],"task":[7],"in":[8,61,226],"the":[9,79,107,161,230],"field":[10,140],"of":[11,222],"remote":[12],"sensing":[13],"with":[14,83,131],"significant":[15],"practical":[16],"value.":[17],"However,":[18],"most":[19],"convolutional":[20],"neural":[21],"network":[22,88,91],"(CNN)-based":[23],"methods":[24,41],"primarily":[25],"focus":[26,186],"on":[27,178,188,229],"capturing":[28],"local":[29,53,95,212],"spatial":[30,54,59,104,125,158,164,171],"features":[31,98],"while":[32,191],"overlooking":[33],"global":[34,97,214],"context.":[35],"In":[36,65,197],"contrast,":[37],"vision":[38],"transformer":[39,82,152],"(ViT)-based":[40],"can":[42,92],"effectively":[43],"model":[44,156],"long-range":[45,157],"dependencies":[46,159],"but":[47],"fail":[48],"to":[49,121,141,155,185,194,208],"adequately":[50],"capture":[51],"detailed":[52],"structures":[55],"and":[56,96,99,126,173,213,237,249],"lack":[57],"explicit":[58,170],"priors":[60,172],"their":[62],"self-attention":[63,81,151],"mechanism.":[64,109],"this":[66],"paper,":[67],"we":[68,111,147],"propose":[69],"novel":[71],"CNN\u2013ViT":[72],"hybrid":[73],"architecture":[74],"for":[75,218],"HSI":[76],"classification,":[77],"called":[78],"Manhattan":[80,150],"adaptive":[84,200],"global\u2013local":[85],"channel":[86,202,215],"attention":[87,108,175,193,203],"(MTACANet).":[89],"This":[90],"simultaneously":[93],"extract":[94,123],"incorporates":[100,169],"an":[101,137,199],"explicit,":[102],"distance-based":[103],"prior":[105],"into":[106],"First,":[110],"introduce":[112],"wavelet-guided":[114],"multi-scale":[115,124,163],"spatial-spectral":[116],"feature":[117,143,165],"extraction":[118,221],"(WMSFE)":[119],"block":[120],"separately":[122],"spectral":[127,224],"features,":[128],"which":[129],"begins":[130],"wavelet":[132],"transform":[133],"convolution":[134],"(WTConv)":[135],"featuring":[136],"expanded":[138],"receptive":[139],"strengthen":[142],"representation":[144],"capabilities.":[145],"Second,":[146],"use":[148],"encoder":[153],"(MSATE)":[154],"across":[160],"extracted":[162],"representations.":[166],"The":[167],"MSATE":[168],"adjusts":[174],"weights":[176],"based":[177],"inter-token":[179],"distances,":[180],"enabling":[181],"each":[182],"target":[183],"token":[184],"more":[187,219],"nearby":[189],"tokens":[190],"suppressing":[192],"distant":[195],"ones.":[196],"addition,":[198],"global-local":[201],"(AGLCA)":[204],"module":[205],"employed":[207],"facilitate":[209],"interactions":[210],"between":[211],"information,":[216],"allowing":[217],"precise":[220],"fine-grained":[223],"characteristics":[225],"HSIs.":[227],"Experiments":[228],"Indian":[231],"Pines,":[232],"GF-5":[233],"Yancheng,":[234],"ZY1-02D":[235],"Huanghekou,":[236],"WHU-Hi-HongHu":[238],"datasets":[239],"show":[240],"that":[241],"its":[242],"overall":[243],"accuracy":[244],"reaches":[245],"99.11%,":[246],"99.28%,":[247],"99.09%,":[248],"95.57%,":[250],"respectively,":[251],"exhibiting":[252],"excellent":[253],"performance.":[254],"Our":[255],"code":[256],"available":[258],"at:":[259],"https://github.com/zhe-meng/MTACANet.":[260]},"counts_by_year":[],"updated_date":"2026-04-04T06:10:10.580331","created_date":"2026-01-29T00:00:00"}