{"id":"https://openalex.org/W4407466425","doi":"https://doi.org/10.1109/iccvit63928.2024.10872424","title":"Multi-Scale and Multi-Modal Contrastive Learning for Hyperspectral and LiDAR Classification","display_name":"Multi-Scale and Multi-Modal Contrastive Learning for Hyperspectral and LiDAR Classification","publication_year":2024,"publication_date":"2024-11-24","ids":{"openalex":"https://openalex.org/W4407466425","doi":"https://doi.org/10.1109/iccvit63928.2024.10872424"},"language":"en","primary_location":{"id":"doi:10.1109/iccvit63928.2024.10872424","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvit63928.2024.10872424","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 2nd International Conference on Computer, Vision and Intelligent Technology (ICCVIT)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5084953118","display_name":"Ping Tan","orcid":"https://orcid.org/0000-0002-4506-6973"},"institutions":[{"id":"https://openalex.org/I100286613","display_name":"Hunan Institute of Science and Technology","ror":"https://ror.org/044ysd349","country_code":"CN","type":"education","lineage":["https://openalex.org/I100286613"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Tan","raw_affiliation_strings":["Hunan Institute of Science and Technology,School of Information Science and Engineer,YueYang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan Institute of Science and Technology,School of Information Science and Engineer,YueYang,China","institution_ids":["https://openalex.org/I100286613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059344854","display_name":"Jia Li","orcid":"https://orcid.org/0000-0002-4346-8696"},"institutions":[{"id":"https://openalex.org/I100286613","display_name":"Hunan Institute of Science and Technology","ror":"https://ror.org/044ysd349","country_code":"CN","type":"education","lineage":["https://openalex.org/I100286613"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Li","raw_affiliation_strings":["Hunan Institute of Science and Technology,School of Information Science and Engineer,YueYang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan Institute of Science and Technology,School of Information Science and Engineer,YueYang,China","institution_ids":["https://openalex.org/I100286613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042231008","display_name":"Guoyun Zhang","orcid":"https://orcid.org/0000-0002-1034-2114"},"institutions":[{"id":"https://openalex.org/I100286613","display_name":"Hunan Institute of Science and Technology","ror":"https://ror.org/044ysd349","country_code":"CN","type":"education","lineage":["https://openalex.org/I100286613"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoyun Zhang","raw_affiliation_strings":["Hunan Institute of Science and Technology,School of Information Science and Engineer,YueYang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan Institute of Science and Technology,School of Information Science and Engineer,YueYang,China","institution_ids":["https://openalex.org/I100286613"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008778016","display_name":"Lin Zhao","orcid":"https://orcid.org/0000-0003-3514-4330"},"institutions":[{"id":"https://openalex.org/I100286613","display_name":"Hunan Institute of Science and Technology","ror":"https://ror.org/044ysd349","country_code":"CN","type":"education","lineage":["https://openalex.org/I100286613"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Zhao","raw_affiliation_strings":["Hunan Institute of Science and Technology,School of Information Science and Engineer,YueYang,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hunan Institute of Science and Technology,School of Information Science and Engineer,YueYang,China","institution_ids":["https://openalex.org/I100286613"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I100286613"],"apc_list":null,"apc_paid":null,"fwci":0.7967,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.77397324,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9334999918937683,"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":0.9334999918937683,"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":0.853492021560669},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.7664423584938049},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6652098894119263},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5620567798614502},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.5600813627243042},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5103639960289001},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4937053620815277},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34529584646224976},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.23399406671524048},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1284312903881073},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.0700390636920929},{"id":"https://openalex.org/keywords/materials-science","display_name":"Materials science","score":0.06907960772514343}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.853492021560669},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.7664423584938049},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6652098894119263},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5620567798614502},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.5600813627243042},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5103639960289001},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4937053620815277},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34529584646224976},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.23399406671524048},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1284312903881073},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0700390636920929},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.06907960772514343},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccvit63928.2024.10872424","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvit63928.2024.10872424","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 2nd International Conference on Computer, Vision and Intelligent Technology (ICCVIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.5299999713897705,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320311213","display_name":"Education Department of Hunan Province","ror":"https://ror.org/05ckg3w11"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322843","display_name":"Natural Science Foundation of\u00a0Hunan Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W1976416886","https://openalex.org/W2377273231","https://openalex.org/W2765739551","https://openalex.org/W2765904812","https://openalex.org/W2919174461","https://openalex.org/W3004968762","https://openalex.org/W3035356612","https://openalex.org/W3048631361","https://openalex.org/W3081753142","https://openalex.org/W3132113525","https://openalex.org/W3208935369","https://openalex.org/W4312465065","https://openalex.org/W4401732412","https://openalex.org/W4402832404"],"related_works":["https://openalex.org/W2385371209","https://openalex.org/W2351984678","https://openalex.org/W4250051149","https://openalex.org/W2140032575","https://openalex.org/W2083270190","https://openalex.org/W2011860471","https://openalex.org/W2012196540","https://openalex.org/W3011451421","https://openalex.org/W2139939267","https://openalex.org/W1974511032"],"abstract_inverted_index":{"The":[0,81],"combination":[1],"of":[2,17,50,56,76,86,157],"hyperspectral":[3],"imagery":[4],"(HSI)":[5],"and":[6,9,20,23,60,78,89,97,108,132,160,167],"light":[7],"detection":[8],"ranging":[10],"(LiDAR)":[11],"data":[12,58],"enables":[13],"the":[14,34,48,54,73,93,110,116,134,137,149,153,163],"simultaneous":[15],"exploitation":[16],"rich":[18],"spectral":[19],"elevation":[21],"information":[22],"has":[24],"become":[25],"a":[26,44,65,119],"hot":[27],"research":[28],"topic":[29],"in":[30],"land-cover":[31],"classification.":[32],"However,":[33],"reliance":[35],"on":[36,162],"large":[37],"datasets":[38],"with":[39,146],"costly":[40],"manual":[41],"annotations":[42],"presents":[43],"significant":[45],"challenge":[46],"to":[47,103,127],"scalability":[49],"models.":[51],"To":[52],"address":[53],"issues":[55],"labeled":[57],"scarcity":[59],"model":[61,82,135],"generalization,":[62],"we":[63],"propose":[64],"dual-branch":[66],"multi-scale":[67],"contrastive":[68,99],"learning":[69],"(DMCL)":[70],"framework":[71,151],"for":[72,136],"joint":[74,138],"classification":[75,139],"HSI":[77],"LiDAR":[79],"data.":[80],"training":[83],"process":[84],"consists":[85],"self-supervised":[87],"pre-training":[88,94],"supervised":[90],"fine-tuning.":[91],"During":[92],"phase,":[95],"intra-modal":[96],"inter-modal":[98],"tasks":[100],"are":[101],"exploited":[102],"learn":[104],"multi-modal":[105],"invariant":[106],"features":[107],"capture":[109],"correlations":[111],"between":[112],"different":[113],"modalities.":[114],"In":[115],"fine-tuning":[117],"stage,":[118],"simple":[120],"yet":[121],"effective":[122,130],"fusion":[123],"module":[124],"is":[125],"introduced":[126],"obtain":[128],"more":[129],"representations":[131],"adapt":[133],"task.":[140],"Experimental":[141],"results":[142],"show":[143],"that,":[144],"compared":[145],"other":[147],"methods,":[148],"DMCL":[150],"achieves":[152],"highest":[154],"overall":[155],"accuracy":[156],"88.68%,":[158],"98.09%,":[159],"76.25%":[161],"Houston":[164],"2013,":[165],"Trento,":[166],"MUUFL":[168],"datasets,":[169],"respectively.":[170]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
