{"id":"https://openalex.org/W4388739256","doi":"https://doi.org/10.1109/tgrs.2023.3333401","title":"Large Kernel Sparse ConvNet Weighted by Multi-Frequency Attention for Remote Sensing Scene Understanding","display_name":"Large Kernel Sparse ConvNet Weighted by Multi-Frequency Attention for Remote Sensing Scene Understanding","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4388739256","doi":"https://doi.org/10.1109/tgrs.2023.3333401"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2023.3333401","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3333401","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":true,"oa_status":"green","oa_url":"https://hal.science/hal-04473702v1/document","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100395814","display_name":"Junjie Wang","orcid":"https://orcid.org/0000-0003-0256-8284"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junjie Wang","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, and the Beijing Kay Laboratory of Fractional Signals and Systems, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, and the Beijing Kay Laboratory of Fractional Signals and Systems, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100317994","display_name":"Wei Li","orcid":"https://orcid.org/0000-0001-7015-7335"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Li","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, and the Beijing Kay Laboratory of Fractional Signals and Systems, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-7015-7335","affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, and the Beijing Kay Laboratory of Fractional Signals and Systems, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100442192","display_name":"Mengmeng Zhang","orcid":"https://orcid.org/0000-0002-5724-9785"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengmeng Zhang","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, and the Beijing Kay Laboratory of Fractional Signals and Systems, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-5724-9785","affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, and the Beijing Kay Laboratory of Fractional Signals and Systems, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5106124934","display_name":"Jocelyn Chanussot","orcid":"https://orcid.org/0000-0003-4817-2875"},"institutions":[{"id":"https://openalex.org/I106785703","display_name":"Institut polytechnique de Grenoble","ror":"https://ror.org/05sbt2524","country_code":"FR","type":"education","lineage":["https://openalex.org/I106785703","https://openalex.org/I899635006"]},{"id":"https://openalex.org/I1294671590","display_name":"Centre National de la Recherche Scientifique","ror":"https://ror.org/02feahw73","country_code":"FR","type":"government","lineage":["https://openalex.org/I1294671590"]},{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en sciences et technologies du num\u00e9rique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283"]},{"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/I4210149092","display_name":"Laboratoire Jean Kuntzmann","ror":"https://ror.org/04ett5b41","country_code":"FR","type":"facility","lineage":["https://openalex.org/I106785703","https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I4210149092","https://openalex.org/I899635006","https://openalex.org/I899635006"]},{"id":"https://openalex.org/I899635006","display_name":"Universit\u00e9 Grenoble Alpes","ror":"https://ror.org/02rx3b187","country_code":"FR","type":"education","lineage":["https://openalex.org/I899635006"]}],"countries":["CN","FR"],"is_corresponding":false,"raw_author_name":"Jocelyn Chanussot","raw_affiliation_strings":["LJK, CNRS, INRIA, Grenoble INP, University of Grenoble Alpes, Grenoble, France","Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-4817-2875","affiliations":[{"raw_affiliation_string":"LJK, CNRS, INRIA, Grenoble INP, University of Grenoble Alpes, Grenoble, France","institution_ids":["https://openalex.org/I106785703","https://openalex.org/I1294671590","https://openalex.org/I1326498283","https://openalex.org/I4210149092","https://openalex.org/I899635006"]},{"raw_affiliation_string":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.321,"has_fulltext":true,"cited_by_count":41,"citation_normalized_percentile":{"value":0.95143053,"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":0.9997000098228455,"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":0.9997000098228455,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9983000159263611,"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"}},{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9968000054359436,"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":0.8520641326904297},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.772942841053009},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7061233520507812},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6701422929763794},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6053450703620911},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5813917517662048},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.54266756772995},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.5291963815689087},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4339578151702881},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41169166564941406}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8520641326904297},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.772942841053009},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7061233520507812},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6701422929763794},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6053450703620911},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5813917517662048},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.54266756772995},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5291963815689087},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4339578151702881},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41169166564941406},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tgrs.2023.3333401","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3333401","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"},{"id":"pmh:oai:HAL:hal-04473702v1","is_oa":true,"landing_page_url":"https://hal.science/hal-04473702","pdf_url":"https://hal.science/hal-04473702v1/document","source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing, 2023, 61, pp.5626112. &#x27E8;10.1109/TGRS.2023.3333401&#x27E9;","raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-04473702v1","is_oa":true,"landing_page_url":"https://hal.science/hal-04473702","pdf_url":"https://hal.science/hal-04473702v1/document","source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing, 2023, 61, pp.5626112. &#x27E8;10.1109/TGRS.2023.3333401&#x27E9;","raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[{"score":0.7300000190734863,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G1041279674","display_name":"MIAI @ Grenoble Alpes","funder_award_id":"ANR-19-P3IA-0003","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"},{"id":"https://openalex.org/G2645103404","display_name":null,"funder_award_id":"2021YFB3900502","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320320883","display_name":"Agence Nationale de la Recherche","ror":"https://ror.org/00rbzpz17"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4388739256.pdf","grobid_xml":"https://content.openalex.org/works/W4388739256.grobid-xml"},"referenced_works_count":65,"referenced_works":["https://openalex.org/W258213556","https://openalex.org/W1566135517","https://openalex.org/W1584663654","https://openalex.org/W1980038761","https://openalex.org/W1989316905","https://openalex.org/W2024106504","https://openalex.org/W2089156630","https://openalex.org/W2100495367","https://openalex.org/W2105464873","https://openalex.org/W2128728535","https://openalex.org/W2145072179","https://openalex.org/W2163352848","https://openalex.org/W2213075807","https://openalex.org/W2342880667","https://openalex.org/W2515866431","https://openalex.org/W2521759377","https://openalex.org/W2592962403","https://openalex.org/W2607558879","https://openalex.org/W2620429297","https://openalex.org/W2752782242","https://openalex.org/W2787070260","https://openalex.org/W2799466885","https://openalex.org/W2807872667","https://openalex.org/W2890732922","https://openalex.org/W2919352650","https://openalex.org/W2946057160","https://openalex.org/W2954156245","https://openalex.org/W2962217138","https://openalex.org/W2964231884","https://openalex.org/W2964350391","https://openalex.org/W2974770574","https://openalex.org/W2998002262","https://openalex.org/W3015063979","https://openalex.org/W3034552520","https://openalex.org/W3035984349","https://openalex.org/W3080919303","https://openalex.org/W3101627915","https://openalex.org/W3105577662","https://openalex.org/W3121523901","https://openalex.org/W3128592650","https://openalex.org/W3155649272","https://openalex.org/W3159088339","https://openalex.org/W3172075653","https://openalex.org/W3202187941","https://openalex.org/W3213288046","https://openalex.org/W4210730494","https://openalex.org/W4212897988","https://openalex.org/W4214910799","https://openalex.org/W4226060438","https://openalex.org/W4226291728","https://openalex.org/W4226334005","https://openalex.org/W4280490589","https://openalex.org/W4285505614","https://openalex.org/W4288391342","https://openalex.org/W4323896850","https://openalex.org/W4362014056","https://openalex.org/W4362500735","https://openalex.org/W4378979722","https://openalex.org/W4382371057","https://openalex.org/W4384521492","https://openalex.org/W4385819914","https://openalex.org/W6696879442","https://openalex.org/W6697212559","https://openalex.org/W6703827677","https://openalex.org/W6842806116"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W147410782","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W3103989898","https://openalex.org/W2180954594","https://openalex.org/W2810679507"],"abstract_inverted_index":{"Remote":[0],"sensing":[1,25,231],"scene":[2],"understanding":[3],"is":[4,121,156,187],"a":[5,14,40,61,111,138,170,181],"highly":[6],"challenging":[7],"task,":[8],"and":[9,86,211],"has":[10,35],"gradually":[11],"emerged":[12],"as":[13,196],"research":[15],"hotspot":[16],"in":[17,58,104],"the":[18,28,45,51,74,84,89,92,99,105,161,204,209,215,218,223,237],"field":[19,64],"of":[20,23,30,47,53,88,101,214],"intelligent":[21],"interpretation":[22],"remote":[24,230],"data.":[26],"Recently,":[27],"use":[29],"convolutional":[31,56,80,126,134,167],"neural":[32,127],"networks":[33],"(CNNs)":[34],"been":[36],"proven":[37],"to":[38,69,98,136,158,189,197,236],"be":[39],"fruitful":[41],"advancement.":[42],"However,":[43],"with":[44],"emergence":[46],"visual":[48],"transformers":[49],"(ViTs),":[50],"limitations":[52],"traditional":[54,125],"small":[55],"kernels":[57,135],"directly":[59],"capturing":[60,175],"large":[62,139],"receptive":[63],"have":[65,82],"posed":[66],"significant":[67],"challenges":[68],"their":[70],"dominant":[71],"role.":[72],"Additionally,":[73],"fixed":[75,162],"neuron":[76,163],"connections":[77,164],"between":[78,165],"different":[79,166],"layers":[81],"weakened":[83],"practicality":[85],"adaptability":[87],"models.":[90],"Furthermore,":[91],"global":[93,191],"average":[94,192],"pooling":[95,193],"also":[96],"leads":[97],"loss":[100],"effective":[102],"information":[103,201],"acquired":[106],"features.":[107],"In":[108,217],"this":[109],"work,":[110],"Large":[112],"kernel":[113],"Sparse":[114],"ConvNet":[115],"weighted":[116],"by":[117],"Multi-frequency":[118],"Attention":[119],"(LSCNet)":[120],"proposed.":[122],"Firstly,":[123],"unlike":[124],"networks,":[128],"it":[129],"utilizes":[130],"two":[131],"parallel":[132],"rectangular":[133],"approximate":[137],"kernel,":[140],"achieving":[141,169],"comparable":[142],"or":[143],"even":[144],"better":[145],"results":[146,226],"than":[147],"ViTs-based":[148,241],"methods.":[149],"Secondly,":[150],"an":[151],"adaptive":[152],"sparse":[153],"optimization":[154],"strategy":[155],"employed":[157],"dynamically":[159],"optimize":[160],"layers,":[168],"favorable":[171],"connectivity":[172],"pattern":[173],"for":[174],"abstract":[176],"features":[177],"more":[178,199],"accurately.":[179],"Lastly,":[180],"novel":[182],"multi-frequency":[183],"attention":[184],"(MFA)":[185],"module":[186],"used":[188],"replace":[190],"(GAP),":[194],"so":[195],"preserve":[198],"useful":[200],"while":[202],"weighting":[203],"recognition":[205,225],"features,":[206],"thereby":[207],"enhancing":[208],"discriminative":[210],"learning":[212],"abilities":[213],"model.":[216],"conducted":[219],"experiments,":[220],"LSCNet":[221],"achieves":[222],"best":[224],"on":[227],"three":[228],"well-known":[229],"aerial":[232],"datasets":[233],"when":[234],"compared":[235],"state-of-the-art":[238],"methods":[239],"(including":[240],"methods).":[242]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":33},{"year":2023,"cited_by_count":1}],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2025-10-10T00:00:00"}
