{"id":"https://openalex.org/W4315473866","doi":"https://doi.org/10.1109/tgrs.2023.3235819","title":"Multiscale and Cross-Level Attention Learning for Hyperspectral Image Classification","display_name":"Multiscale and Cross-Level Attention Learning for Hyperspectral Image Classification","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4315473866","doi":"https://doi.org/10.1109/tgrs.2023.3235819"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2023.3235819","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3235819","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/A5033997795","display_name":"Fulin Xu","orcid":"https://orcid.org/0000-0002-1756-9936"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fulin Xu","raw_affiliation_strings":["School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100326078","display_name":"Ge Zhang","orcid":"https://orcid.org/0000-0003-1308-5149"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ge Zhang","raw_affiliation_strings":["School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025021168","display_name":"Chao Song","orcid":"https://orcid.org/0000-0002-7695-7904"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Song","raw_affiliation_strings":["School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100460863","display_name":"Hui Wang","orcid":"https://orcid.org/0000-0002-9567-6227"},"institutions":[{"id":"https://openalex.org/I4210088244","display_name":"Shanghai Micro Satellite Engineering Center","ror":"https://ror.org/003cp7918","country_code":"CN","type":"nonprofit","lineage":["https://openalex.org/I4210088244"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Wang","raw_affiliation_strings":["Key Laboratory of Millimeter Wave Imaging Technology, Shanghai Institute of Satellites Engineering, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Millimeter Wave Imaging Technology, Shanghai Institute of Satellites Engineering, Shanghai, China","institution_ids":["https://openalex.org/I4210088244"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067207818","display_name":"Shaohui Mei","orcid":"https://orcid.org/0000-0002-8018-596X"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaohui Mei","raw_affiliation_strings":["School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China"],"affiliations":[{"raw_affiliation_string":"School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5033997795"],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":null,"apc_paid":null,"fwci":12.7556,"has_fulltext":false,"cited_by_count":81,"citation_normalized_percentile":{"value":0.99024041,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"61","issue":null,"first_page":"1","last_page":"15"},"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":0.9961000084877014,"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":0.9781000018119812,"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":0.8759511113166809},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7783106565475464},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6840313673019409},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6259317398071289},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5672444701194763},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5578323602676392},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5384241342544556},{"id":"https://openalex.org/keywords/spatial-contextual-awareness","display_name":"Spatial contextual awareness","score":0.46079713106155396},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.45749956369400024},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.45544978976249695},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.45470482110977173},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4127585291862488},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.41268062591552734},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.22931638360023499}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8759511113166809},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7783106565475464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6840313673019409},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6259317398071289},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5672444701194763},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5578323602676392},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5384241342544556},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.46079713106155396},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.45749956369400024},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.45544978976249695},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.45470482110977173},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4127585291862488},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.41268062591552734},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.22931638360023499},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2023.3235819","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3235819","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":[{"score":0.6600000262260437,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[{"id":"https://openalex.org/G5521239382","display_name":null,"funder_award_id":"62171381","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/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W1950365613","https://openalex.org/W1966580635","https://openalex.org/W2004104348","https://openalex.org/W2018257962","https://openalex.org/W2029316659","https://openalex.org/W2051253084","https://openalex.org/W2059217921","https://openalex.org/W2097092275","https://openalex.org/W2164330327","https://openalex.org/W2548340849","https://openalex.org/W2558098092","https://openalex.org/W2602024454","https://openalex.org/W2611655888","https://openalex.org/W2764034829","https://openalex.org/W2764276316","https://openalex.org/W2767651786","https://openalex.org/W2782517596","https://openalex.org/W2782522152","https://openalex.org/W2792332881","https://openalex.org/W2793941577","https://openalex.org/W2808098982","https://openalex.org/W2914331134","https://openalex.org/W2919115771","https://openalex.org/W2942454403","https://openalex.org/W2962770389","https://openalex.org/W2971432438","https://openalex.org/W2989871747","https://openalex.org/W2991616716","https://openalex.org/W3002092414","https://openalex.org/W3006984222","https://openalex.org/W3012548728","https://openalex.org/W3028636477","https://openalex.org/W3031015423","https://openalex.org/W3031696400","https://openalex.org/W3043181422","https://openalex.org/W3043183554","https://openalex.org/W3046007115","https://openalex.org/W3095567726","https://openalex.org/W3098388691","https://openalex.org/W3105357426","https://openalex.org/W3128776197","https://openalex.org/W3137868257","https://openalex.org/W3171853541","https://openalex.org/W3175672129","https://openalex.org/W3191251640","https://openalex.org/W3200959564","https://openalex.org/W3202179271","https://openalex.org/W3205838879","https://openalex.org/W3206058291","https://openalex.org/W3214821343","https://openalex.org/W4200482478","https://openalex.org/W4200525139","https://openalex.org/W4210794570","https://openalex.org/W4220853886","https://openalex.org/W4226438862","https://openalex.org/W4285127355","https://openalex.org/W4289752563","https://openalex.org/W4293661538","https://openalex.org/W4296339430"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3000097931","https://openalex.org/W3034375524","https://openalex.org/W2354322770","https://openalex.org/W4230131218","https://openalex.org/W2404757046","https://openalex.org/W3083508853","https://openalex.org/W3217780232"],"abstract_inverted_index":{"Transformer-based":[0],"networks,":[1],"which":[2,142],"can":[3],"well":[4],"model":[5],"the":[6,13,30,69,87,100,117,123,127,136,144,163,169],"global":[7,70],"characteristics":[8],"of":[9,44,75,84,120,139],"inputted":[10],"data":[11],"using":[12,122],"attention":[14,59,124,129],"mechanism,":[15],"have":[16],"been":[17],"widely":[18],"applied":[19],"to":[20,34,65,112,135],"hyperspectral":[21,47,154,179],"image":[22],"(HSI)":[23],"classification":[24],"and":[25,57,71,97,147,174],"achieved":[26],"promising":[27],"results.":[28],"However,":[29],"existing":[31],"networks":[32,177],"fail":[33],"explore":[35,67],"complex":[36],"local":[37,72,81],"land":[38],"cover":[39],"structures":[40],"in":[41,46,86],"different":[42],"scales":[43],"shapes":[45],"remote":[48],"sensing":[49],"images.":[50],"Therefore,":[51],"a":[52,89,104],"novel":[53],"network":[54,62,172],"named":[55],"multiscale":[56,73,90],"cross-level":[58,105],"learning":[60],"(MCAL)":[61],"is":[63,95,110,132],"proposed":[64,111,164],"fully":[66],"both":[68,143,168],"features":[74,115],"pixels":[76,85],"for":[77,153,178],"classification.":[78,155,180],"To":[79],"encounter":[80],"spatial":[82,145],"context":[83,146],"transformer,":[88],"feature":[91,106],"extraction":[92],"(MSFE)":[93],"module":[94,109,130],"constructed":[96],"implemented":[98,133],"into":[99],"transformer-based":[101,175],"networks.":[102],"Moreover,":[103],"fusion":[107],"(CLFF)":[108],"adaptively":[113],"fuse":[114],"from":[116],"hierarchical":[118,137],"structure":[119,138],"MSFEs":[121],"mechanism.":[125],"Finally,":[126],"spectral":[128,148],"(SAM)":[131],"prior":[134],"MSFEs,":[140],"by":[141],"information":[149],"are":[150],"jointly":[151],"emphasized":[152],"Experiments":[156],"over":[157],"several":[158],"benchmark":[159],"datasets":[160],"demonstrate":[161],"that":[162],"MCAL":[165],"obviously":[166],"outperforms":[167],"convolutional":[170],"neural":[171],"(CNN)-based":[173],"state-of-the-art":[176]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":31},{"year":2024,"cited_by_count":43},{"year":2023,"cited_by_count":5}],"updated_date":"2026-04-11T08:14:18.477133","created_date":"2025-10-10T00:00:00"}