{"id":"https://openalex.org/W4411949910","doi":"https://doi.org/10.1109/tgrs.2025.3585446","title":"A Novel Transformer-KAN Network Utilizes Multimodality Contrastive Learning and Mask Reconstruction for Remote Sensing Classification","display_name":"A Novel Transformer-KAN Network Utilizes Multimodality Contrastive Learning and Mask Reconstruction for Remote Sensing Classification","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4411949910","doi":"https://doi.org/10.1109/tgrs.2025.3585446"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2025.3585446","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3585446","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/A5074326682","display_name":"Shuxiang Xia","orcid":"https://orcid.org/0009-0008-7795-1361"},"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":"Shuxiang Xia","raw_affiliation_strings":["Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China","School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0009-0008-7795-1361","affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100445822","display_name":"Xiaohua Zhang","orcid":"https://orcid.org/0009-0007-6783-4586"},"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":"Xiaohua Zhang","raw_affiliation_strings":["Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China","School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0009-0007-6783-4586","affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hongyun Meng","orcid":"https://orcid.org/0009-0008-2172-0089"},"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":"Hongyun Meng","raw_affiliation_strings":["School of Mathematics and Statistics, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0009-0008-2172-0089","affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, 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":"https://orcid.org/0000-0003-3354-9617"},"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":["Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China","School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0003-3354-9617","affiliations":[{"raw_affiliation_string":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Artificial Intelligence, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1942222,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"63","issue":null,"first_page":"1","last_page":"16"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.954800009727478,"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":0.954800009727478,"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/computer-science","display_name":"Computer science","score":0.699433445930481},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.5969160199165344},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5399231314659119},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5337582230567932},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4837034344673157},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3615461587905884},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3257991075515747},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.1749153435230255},{"id":"https://openalex.org/keywords/voltage","display_name":"Voltage","score":0.12325853109359741},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11891332268714905},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.11395034193992615}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.699433445930481},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.5969160199165344},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5399231314659119},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5337582230567932},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4837034344673157},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3615461587905884},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3257991075515747},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.1749153435230255},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.12325853109359741},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11891332268714905},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.11395034193992615}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2025.3585446","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3585446","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/G7886416700","display_name":null,"funder_award_id":"61877066","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G795180829","display_name":null,"funder_award_id":"21RGZN0010","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2744049245","https://openalex.org/W2798991696","https://openalex.org/W2842511635","https://openalex.org/W2908968031","https://openalex.org/W3035524453","https://openalex.org/W3081753142","https://openalex.org/W3111935347","https://openalex.org/W3138516171","https://openalex.org/W3208935369","https://openalex.org/W4211066633","https://openalex.org/W4213019189","https://openalex.org/W4214598159","https://openalex.org/W4223616928","https://openalex.org/W4225158991","https://openalex.org/W4283776488","https://openalex.org/W4285107185","https://openalex.org/W4309367935","https://openalex.org/W4312208437","https://openalex.org/W4312339456","https://openalex.org/W4312465065","https://openalex.org/W4312989292","https://openalex.org/W4315606133","https://openalex.org/W4319991308","https://openalex.org/W4361982595","https://openalex.org/W4366504084","https://openalex.org/W4385245566","https://openalex.org/W4386145024","https://openalex.org/W4386432259","https://openalex.org/W4388037272","https://openalex.org/W4389542636","https://openalex.org/W4389827562","https://openalex.org/W4390232172","https://openalex.org/W4390561780","https://openalex.org/W4391676180","https://openalex.org/W4392578770","https://openalex.org/W4393006203","https://openalex.org/W4396613334","https://openalex.org/W4396685392","https://openalex.org/W4399849901","https://openalex.org/W4400020303","https://openalex.org/W4400062116","https://openalex.org/W4400352962","https://openalex.org/W4400724905","https://openalex.org/W4400975346","https://openalex.org/W4401809840","https://openalex.org/W4402521085","https://openalex.org/W4403780847","https://openalex.org/W4403938813","https://openalex.org/W4403969343","https://openalex.org/W4405747025","https://openalex.org/W6759916787","https://openalex.org/W6771626834","https://openalex.org/W6874655442"],"related_works":["https://openalex.org/W2385859805","https://openalex.org/W2530972254","https://openalex.org/W2374013449","https://openalex.org/W73545470","https://openalex.org/W2364381299","https://openalex.org/W2374430585","https://openalex.org/W3144423903","https://openalex.org/W2377397762","https://openalex.org/W2793967660","https://openalex.org/W2361654993"],"abstract_inverted_index":{"Contrastive":[0],"learning":[1,167],"is":[2],"a":[3,80,128,148],"promising":[4],"paradigm":[5],"in":[6,39,70,195],"the":[7,21,65,90,93,96,105,114,159,180],"multimodal":[8],"few-shot":[9],"domain,":[10],"leveraging":[11],"images":[12],"captured":[13],"from":[14],"different":[15],"sensors":[16],"as":[17,47,92],"diverse":[18],"perspectives":[19],"of":[20,68,184],"same":[22,97],"ground":[23],"objects.":[24],"However,":[25],"existing":[26],"methods":[27,49],"encounter":[28],"two":[29],"major":[30],"challenges:":[31,86],"relatively":[32],"weak":[33],"discriminative":[34],"ability,":[35],"complex":[36],"features":[37,52],"inherent":[38],"remote":[40,72,197],"sensing":[41,73,198],"data,":[42],"and":[43,134,138,146,165,191],"insufficient":[44],"cross-modal":[45],"interactions,":[46],"these":[48,85],"often":[50],"align":[51,133],"within":[53],"individual":[54],"modalities":[55],"without":[56,119],"fully":[57],"exploiting":[58],"their":[59,143],"complementary":[60,144],"information.":[61],"These":[62],"limitations":[63],"hinder":[64],"overall":[66],"performance":[67,183],"models":[69],"multi-modal":[71,170,196],"tasks.":[74,199],"In":[75],"this":[76],"paper,":[77],"we":[78,88,99,126],"propose":[79],"novel":[81],"framework":[82],"to":[83,132,152],"address":[84],"First,":[87],"introduce":[89],"Transformer":[91],"encoder,":[94],"at":[95],"time,":[98],"replace":[100],"traditional":[101],"multilayer":[102],"perceptrons":[103],"with":[104],"Kolmogorov\u2013Arnold":[106],"Network":[107],"(KAN)":[108],"network":[109],"for":[110,168],"feature":[111],"dimensionality":[112],"reduction,":[113],"model":[115],"reduces":[116],"parameters":[117],"slightly":[118],"affecting":[120],"accuracy,":[121],"achieving":[122],"better":[123],"efficiency.":[124],"Second,":[125],"design":[127],"specialized":[129],"projection":[130],"layer":[131],"deeply":[135],"integrate":[136],"spatial":[137,164],"LiDAR":[139],"modalities,":[140],"effectively":[141],"harnessing":[142],"strengths":[145],"implementing":[147],"self-supervised":[149],"decoding":[150],"mechanism":[151],"reconstruct":[153],"masked":[154],"image":[155],"patches":[156],"generated":[157],"during":[158],"data":[160],"augmentation":[161],"process,":[162],"enforcing":[163],"structural":[166],"robust":[169],"representations.":[171],"Extensive":[172],"experiments":[173],"conducted":[174],"on":[175],"four":[176],"public":[177],"datasets":[178],"demonstrate":[179],"superior":[181],"classification":[182],"our":[185],"proposed":[186],"method,":[187],"outperforming":[188],"state-of-the-art":[189],"approaches":[190],"confirming":[192],"its":[193],"efficacy":[194]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}