{"id":"https://openalex.org/W4386766977","doi":"https://doi.org/10.1109/tgrs.2023.3315678","title":"Transformer-Based Masked Autoencoder With Contrastive Loss for Hyperspectral Image Classification","display_name":"Transformer-Based Masked Autoencoder With Contrastive Loss for Hyperspectral Image Classification","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4386766977","doi":"https://doi.org/10.1109/tgrs.2023.3315678"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2023.3315678","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3315678","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/A5100681906","display_name":"Xianghai Cao","orcid":"https://orcid.org/0000-0003-0997-4664"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xianghai Cao","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058852174","display_name":"Haifeng Lin","orcid":"https://orcid.org/0000-0002-3835-6075"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haifeng Lin","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102556352","display_name":"Shuaixu Guo","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuaixu Guo","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083680496","display_name":"Tao Xiong","orcid":"https://orcid.org/0000-0002-9773-649X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Xiong","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050630882","display_name":"Licheng Jiao","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Licheng Jiao","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100681906"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":8.871,"has_fulltext":false,"cited_by_count":56,"citation_normalized_percentile":{"value":0.98162454,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"61","issue":null,"first_page":"1","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9925000071525574,"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":0.987500011920929,"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8254827260971069},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6894230246543884},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5860817432403564},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5597241520881653},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5512760281562805},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5273115038871765},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3751644194126129},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33724233508110046},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2700384259223938},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.16033431887626648},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.13117259740829468}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8254827260971069},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6894230246543884},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5860817432403564},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5597241520881653},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5512760281562805},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5273115038871765},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3751644194126129},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33724233508110046},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2700384259223938},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.16033431887626648},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.13117259740829468}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2023.3315678","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3315678","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/G8921911086","display_name":null,"funder_award_id":"62176199","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":63,"referenced_works":["https://openalex.org/W1990895816","https://openalex.org/W2012797058","https://openalex.org/W2039409148","https://openalex.org/W2087263574","https://openalex.org/W2097900616","https://openalex.org/W2164769329","https://openalex.org/W2168478992","https://openalex.org/W2194775991","https://openalex.org/W2609880332","https://openalex.org/W2614256707","https://openalex.org/W2620547787","https://openalex.org/W2754507318","https://openalex.org/W2757242159","https://openalex.org/W2764276316","https://openalex.org/W2768211636","https://openalex.org/W2791006446","https://openalex.org/W2842511635","https://openalex.org/W2896457183","https://openalex.org/W2904698365","https://openalex.org/W2904995961","https://openalex.org/W2914331134","https://openalex.org/W2941387379","https://openalex.org/W2972505576","https://openalex.org/W2998460251","https://openalex.org/W3023351371","https://openalex.org/W3035524453","https://openalex.org/W3047443805","https://openalex.org/W3048631361","https://openalex.org/W3049655825","https://openalex.org/W3064134516","https://openalex.org/W3101846690","https://openalex.org/W3102692100","https://openalex.org/W3103695279","https://openalex.org/W3103753223","https://openalex.org/W3125860323","https://openalex.org/W3128776197","https://openalex.org/W3145450063","https://openalex.org/W3170863103","https://openalex.org/W3171853541","https://openalex.org/W3200020327","https://openalex.org/W3200493442","https://openalex.org/W3205965083","https://openalex.org/W3209540366","https://openalex.org/W3214821343","https://openalex.org/W4206307542","https://openalex.org/W4210794570","https://openalex.org/W4221141078","https://openalex.org/W4240485910","https://openalex.org/W4292825900","https://openalex.org/W4293195393","https://openalex.org/W4294068665","https://openalex.org/W4297808394","https://openalex.org/W4312789082","https://openalex.org/W4313156423","https://openalex.org/W4362519158","https://openalex.org/W4389104669","https://openalex.org/W6739901393","https://openalex.org/W6755207826","https://openalex.org/W6774314701","https://openalex.org/W6779326418","https://openalex.org/W6796761347","https://openalex.org/W6841432550","https://openalex.org/W6842430276"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W2159052453","https://openalex.org/W2072166414","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W2385371209","https://openalex.org/W4250051149","https://openalex.org/W2083270190","https://openalex.org/W2546503577"],"abstract_inverted_index":{"Recent":[0],"years,":[1],"in":[2,68],"order":[3],"to":[4,33,40,63,109,134,153,166,188,211,218],"solve":[5],"the":[6,42,45,50,53,69,76,116,123,136,141,149,155,161,168,172,190,193,199,205,213,219],"problem":[7],"of":[8,29,52,71,79,91,140,171,179,201],"lacking":[9],"accurately":[10],"labeled":[11,54],"hyperspectral":[12,23,72,143,156,233],"image":[13,24,73,138,144,157,191,234],"data,":[14],"self-supervised":[15,30,58,80],"learning":[16,31,59,83,105,210],"has":[17,120,126,131],"become":[18],"an":[19,127,132],"effective":[20],"method":[21],"for":[22],"classification.":[25],"The":[26,175],"core":[27],"idea":[28],"is":[32,164],"define":[34],"a":[35,98,180,184],"pretext":[36],"task":[37],"which":[38,107],"helps":[39],"train":[41],"model":[43,222],"without":[44],"labels.":[46],"By":[47],"exploiting":[48],"both":[49],"information":[51],"and":[55,84,114,145,160,183,204,228],"unlabeled":[56],"samples,":[57],"shows":[60,223],"enormous":[61],"potential":[62],"handle":[64],"many":[65],"different":[66],"tasks":[67],"field":[70],"processing.":[74],"Among":[75],"vast":[77],"amount":[78],"methods,":[81],"contrastive":[82,104,209],"masked":[85,101,142],"autoencoder":[86,102],"are":[87],"well":[88],"known":[89],"because":[90],"their":[92],"impressive":[93],"performance.":[94,216],"This":[95],"article":[96],"proposes":[97],"Transformer":[99],"based":[100],"using":[103],"(TMAC),":[106],"tries":[108],"combine":[110],"these":[111],"two":[112,121,146],"methods":[113],"improve":[115],"performance":[117],"further.":[118],"TMAC":[119],"branches,":[122],"first":[124],"branch":[125,177],"encoder-decoders":[128],"structure,":[129],"it":[130],"encoder":[133,182],"capture":[135],"latent":[137],"representation":[139],"decoders":[147],"where":[148],"pixel":[150],"decoder":[151,163,203],"aims":[152],"reconstruct":[154],"at":[158],"pixel-level":[159],"feature":[162,170,194,202,225],"built":[165],"extract":[167],"high-level":[169],"reconstructed":[173],"image.":[174],"second":[176],"consists":[178],"momentum":[181],"standard":[185],"projection":[186],"head":[187],"embed":[189],"into":[192],"space.":[195],"Then,":[196],"by":[197],"combining":[198],"output":[200],"embedding":[206],"vectors":[207],"via":[208],"enhance":[212],"model\u2019s":[214],"classification":[215],"According":[217],"experiments,":[220],"our":[221],"powerful":[224],"extraction":[226],"capability":[227],"gets":[229],"outstanding":[230],"results":[231],"on":[232],"datasets.":[235]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":22},{"year":2024,"cited_by_count":28},{"year":2023,"cited_by_count":1}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}
