{"id":"https://openalex.org/W4390871472","doi":"https://doi.org/10.1109/lgrs.2024.3354175","title":"Spatial-Gated Multilayer Perceptron for Land Use and Land Cover Mapping","display_name":"Spatial-Gated Multilayer Perceptron for Land Use and Land Cover Mapping","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4390871472","doi":"https://doi.org/10.1109/lgrs.2024.3354175"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2024.3354175","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2024.3354175","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","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/A5046120119","display_name":"Ali Jamali","orcid":"https://orcid.org/0000-0002-6073-5493"},"institutions":[{"id":"https://openalex.org/I18014758","display_name":"Simon Fraser University","ror":"https://ror.org/0213rcc28","country_code":"CA","type":"education","lineage":["https://openalex.org/I18014758"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Ali Jamali","raw_affiliation_strings":["Department of Geography, Simon Fraser University, Burnaby, Canada","Department of Geography, Simon Fraser University, 8888 University Dr, Burnaby, BC, Canada"],"raw_orcid":"https://orcid.org/0000-0002-6073-5493","affiliations":[{"raw_affiliation_string":"Department of Geography, Simon Fraser University, Burnaby, Canada","institution_ids":["https://openalex.org/I18014758"]},{"raw_affiliation_string":"Department of Geography, Simon Fraser University, 8888 University Dr, Burnaby, BC, Canada","institution_ids":["https://openalex.org/I18014758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087427076","display_name":"Swalpa Kumar Roy","orcid":"https://orcid.org/0000-0002-6580-3977"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Swalpa Kumar Roy","raw_affiliation_strings":["Department of Computer Science and Engineering, Alipurduar Government Engineering and Management College, Chhipra, West Bengal, India"],"raw_orcid":"https://orcid.org/0000-0002-6580-3977","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Alipurduar Government Engineering and Management College, Chhipra, West Bengal, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075013625","display_name":"Danfeng Hong","orcid":"https://orcid.org/0000-0002-3212-9584"},"institutions":[{"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"]},{"id":"https://openalex.org/I4210137199","display_name":"Aerospace Information Research Institute","ror":"https://ror.org/0419fj215","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]},{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Danfeng Hong","raw_affiliation_strings":["Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China","School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-3212-9584","affiliations":[{"raw_affiliation_string":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210137199","https://openalex.org/I19820366"]},{"raw_affiliation_string":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000622487","display_name":"Peter M. Atkinson","orcid":"https://orcid.org/0000-0002-5489-6880"},"institutions":[{"id":"https://openalex.org/I67415387","display_name":"Lancaster University","ror":"https://ror.org/04f2nsd36","country_code":"GB","type":"education","lineage":["https://openalex.org/I67415387"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Peter M. Atkinson","raw_affiliation_strings":["Faculty of Science and Technology, Lancaster University, Lancaster, U.K"],"raw_orcid":"https://orcid.org/0000-0002-5489-6880","affiliations":[{"raw_affiliation_string":"Faculty of Science and Technology, Lancaster University, Lancaster, U.K","institution_ids":["https://openalex.org/I67415387"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5074919292","display_name":"Pedram Ghamisi","orcid":"https://orcid.org/0000-0003-1203-741X"},"institutions":[{"id":"https://openalex.org/I161878677","display_name":"Austrian Research Institute for Artificial Intelligence","ror":"https://ror.org/04j47vk14","country_code":"AT","type":"facility","lineage":["https://openalex.org/I161878677","https://openalex.org/I4210107880"]},{"id":"https://openalex.org/I2801798921","display_name":"Helmholtz-Zentrum Dresden-Rossendorf","ror":"https://ror.org/01zy2cs03","country_code":"DE","type":"facility","lineage":["https://openalex.org/I1305996414","https://openalex.org/I2801798921"]},{"id":"https://openalex.org/I4210148560","display_name":"Helmholtz Institute Freiberg for Resource Technology","ror":"https://ror.org/04kdb0j04","country_code":"DE","type":"government","lineage":["https://openalex.org/I1305996414","https://openalex.org/I2801798921","https://openalex.org/I4210148560"]},{"id":"https://openalex.org/I4210157875","display_name":"Institute of Advanced Research in Artificial Intelligence","ror":"https://ror.org/04m8gxe14","country_code":"AT","type":"facility","lineage":["https://openalex.org/I4210157875"]}],"countries":["AT","DE"],"is_corresponding":false,"raw_author_name":"Pedram Ghamisi","raw_affiliation_strings":["Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany","Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria"],"raw_orcid":"https://orcid.org/0000-0003-1203-741X","affiliations":[{"raw_affiliation_string":"Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany","institution_ids":["https://openalex.org/I4210148560","https://openalex.org/I2801798921"]},{"raw_affiliation_string":"Institute of Advanced Research in Artificial Intelligence (IARAI), Vienna, Austria","institution_ids":["https://openalex.org/I161878677","https://openalex.org/I4210157875"]}]}],"institutions":[],"countries_distinct_count":5,"institutions_distinct_count":9,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.3386,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.94639693,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"21","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9988999962806702,"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":0.9988999962806702,"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":0.9922000169754028,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9696000218391418,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.825538158416748},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6944975852966309},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6343451142311096},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5796941518783569},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.56535404920578},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5534008741378784},{"id":"https://openalex.org/keywords/perceptron","display_name":"Perceptron","score":0.4853038191795349},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4635436236858368},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4147574305534363},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4121879041194916},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.3774080276489258},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.34642040729522705},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.32826924324035645},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.1735350787639618},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.08243876695632935}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.825538158416748},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6944975852966309},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6343451142311096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5796941518783569},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.56535404920578},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5534008741378784},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4853038191795349},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4635436236858368},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4147574305534363},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4121879041194916},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3774080276489258},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.34642040729522705},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32826924324035645},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.1735350787639618},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.08243876695632935},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2024.3354175","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2024.3354175","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"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 Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","score":0.7300000190734863,"id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1965309615","https://openalex.org/W1982820123","https://openalex.org/W2194775991","https://openalex.org/W2897043150","https://openalex.org/W2914331134","https://openalex.org/W2923136550","https://openalex.org/W2942454403","https://openalex.org/W2963163009","https://openalex.org/W3122028341","https://openalex.org/W3157506437","https://openalex.org/W3160030872","https://openalex.org/W3168367808","https://openalex.org/W3171087525","https://openalex.org/W3177318507","https://openalex.org/W4281756776","https://openalex.org/W4380763457","https://openalex.org/W4381163482","https://openalex.org/W6786852218","https://openalex.org/W6795140394","https://openalex.org/W6796417832","https://openalex.org/W6796931752","https://openalex.org/W6839178539","https://openalex.org/W6945257545"],"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/W2070598848","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2019190440","https://openalex.org/W2343470940"],"abstract_inverted_index":{"Due":[0],"to":[1,4,76,79,108],"its":[2],"capacity":[3],"recognize":[5],"detailed":[6],"spectral":[7],"differences,":[8],"hyperspectral":[9,149],"data":[10,39,96,144],"have":[11,27,66],"been":[12],"extensively":[13,51],"used":[14],"for":[15,53,134],"precise":[16,135],"Land":[17,19,136,138],"Use":[18,137],"Cover":[20,139],"(LULC)":[21,140],"mapping.":[22],"However,":[23,78],"recent":[24],"multi-modal":[25,143],"methods":[26],"shown":[28],"their":[29,81],"superior":[30,69],"classification":[31,83,159,176],"performance":[32],"over":[33,161],"the":[34,42,54,93,119,153,156,180],"algorithms":[35],"that":[36,125],"use":[37],"single":[38],"sets.":[40],"On":[41],"other":[43],"hand,":[44],"Convolutional":[45],"Neural":[46],"Networks":[47],"(CNNs)":[48],"are":[49],"models":[50],"utilized":[52],"hierarchical":[55],"extraction":[56],"of":[57,71,155],"features.":[58],"Vision":[59],"transformers":[60],"(ViTs),":[61],"through":[62],"a":[63,121],"self-attention":[64],"mechanism,":[65],"recently":[67],"achieved":[68],"modeling":[70],"global":[72],"contextual":[73],"information":[74],"compared":[75],"CNNs.":[77],"harness":[80],"image":[82],"strength,":[84],"ViTs":[85],"require":[86],"substantial":[87],"training":[88,95],"datasets.":[89],"In":[90,114],"cases":[91],"where":[92],"available":[94,192],"is":[97],"limited,":[98],"current":[99],"advanced":[100],"multi-layer":[101],"perceptrons":[102],"(MLPs)":[103],"can":[104],"provide":[105],"viable":[106],"alternatives":[107],"both":[109],"deep":[110,122],"CNNs":[111],"and":[112,129,148,164,172,183],"ViTs.":[113],"this":[115],"paper,":[116],"we":[117],"developed":[118,157],"SGU-MLP,":[120],"learning":[123],"algorithm":[124,160],"effectively":[126],"combines":[127],"MLPs":[128],"spatial":[130],"gating":[131],"units":[132],"(SGUs)":[133],"mapping":[141],"using":[142],"from":[145],"multi-spectral,":[146],"LiDAR,":[147],"data.":[150],"Results":[151],"illustrated":[152],"superiority":[154],"SGU-MLP":[158,175],"several":[162],"CNN":[163,182],"CNN-ViT-based":[165,184],"models,":[166],"including":[167],"HybridSN,":[168],"ResNet,":[169],"iFormer,":[170],"EfficientFormer,":[171],"CoAtNet.":[173],"The":[174,186],"model":[177],"consistently":[178],"outperformed":[179],"benchmark":[181],"algorithms.":[185],"code":[187],"will":[188],"be":[189],"made":[190],"publicly":[191],"at":[193],"https:":[194],"//github.com/aj1365/SGUMLP.":[195]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":7}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
