{"id":"https://openalex.org/W4384915783","doi":"https://doi.org/10.1109/tgrs.2023.3296703","title":"Cross-Modal Contrastive Learning for Remote Sensing Image Classification","display_name":"Cross-Modal Contrastive Learning for Remote Sensing Image Classification","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4384915783","doi":"https://doi.org/10.1109/tgrs.2023.3296703"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2023.3296703","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3296703","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/A5053487344","display_name":"Zhixi Feng","orcid":"https://orcid.org/0000-0002-7372-9180"},"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":"Zhixi Feng","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0002-7372-9180","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043575604","display_name":"Liangliang Song","orcid":"https://orcid.org/0000-0002-2187-727X"},"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":"Liangliang Song","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0002-2187-727X","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100764373","display_name":"Shuyuan Yang","orcid":"https://orcid.org/0000-0002-4796-5737"},"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":"Shuyuan Yang","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0002-4796-5737","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017790584","display_name":"Xinyu Zhang","orcid":"https://orcid.org/0000-0002-5011-5768"},"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":"Xinyu Zhang","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0002-5011-5768","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi&#x2019;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":["School of Artificial Intelligence, Xidian University, Xi&#x2019;an, China"],"raw_orcid":"https://orcid.org/0000-0003-3354-9617","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi&#x2019;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":5.1762,"has_fulltext":false,"cited_by_count":36,"citation_normalized_percentile":{"value":0.96026154,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"61","issue":null,"first_page":"1","last_page":"13"},"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":0.9898999929428101,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9821000099182129,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer 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":0.7731679677963257},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.7493504285812378},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.695794403553009},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6137908101081848},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5561043620109558},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5337734222412109},{"id":"https://openalex.org/keywords/modalities","display_name":"Modalities","score":0.5267890095710754},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5028218626976013},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4801637530326843},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4572522044181824},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4414825439453125},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40702885389328003},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2774519622325897}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7731679677963257},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.7493504285812378},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.695794403553009},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6137908101081848},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5561043620109558},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5337734222412109},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.5267890095710754},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5028218626976013},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4801637530326843},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4572522044181824},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4414825439453125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40702885389328003},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2774519622325897},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","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/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2023.3296703","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2023.3296703","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":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.5899999737739563}],"awards":[{"id":"https://openalex.org/G1560732540","display_name":null,"funder_award_id":"62276205","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6986719856","display_name":null,"funder_award_id":"62171357","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8433512880","display_name":null,"funder_award_id":"2020CFA001, China","funder_id":"https://openalex.org/F4320322186","funder_display_name":"Natural Science Foundation of Hubei Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322186","display_name":"Natural Science Foundation of Hubei Province","ror":null},{"id":"https://openalex.org/F4320327471","display_name":"China Aerospace Science and Technology Corporation","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":55,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1584663654","https://openalex.org/W1976416886","https://openalex.org/W2002392274","https://openalex.org/W2017169137","https://openalex.org/W2054689043","https://openalex.org/W2079299474","https://openalex.org/W2116699851","https://openalex.org/W2136251662","https://openalex.org/W2146062404","https://openalex.org/W2606929568","https://openalex.org/W2614256707","https://openalex.org/W2623518586","https://openalex.org/W2744049245","https://openalex.org/W2764276316","https://openalex.org/W2765739551","https://openalex.org/W2767892373","https://openalex.org/W2796684832","https://openalex.org/W2890133123","https://openalex.org/W2898460625","https://openalex.org/W2907147407","https://openalex.org/W2928260953","https://openalex.org/W2948157022","https://openalex.org/W2965129091","https://openalex.org/W2998142089","https://openalex.org/W3004968762","https://openalex.org/W3005680577","https://openalex.org/W3026303927","https://openalex.org/W3035060554","https://openalex.org/W3035524453","https://openalex.org/W3048631361","https://openalex.org/W3099850646","https://openalex.org/W3102692100","https://openalex.org/W3103753223","https://openalex.org/W3105997607","https://openalex.org/W3135445258","https://openalex.org/W3209540366","https://openalex.org/W3216860461","https://openalex.org/W4210794570","https://openalex.org/W4212897988","https://openalex.org/W4223616928","https://openalex.org/W4225931144","https://openalex.org/W4226275810","https://openalex.org/W4280490589","https://openalex.org/W4283370461","https://openalex.org/W4285124347","https://openalex.org/W4285134520","https://openalex.org/W4293733592","https://openalex.org/W4312592451","https://openalex.org/W4312981890","https://openalex.org/W4385245566","https://openalex.org/W6631190155","https://openalex.org/W6739901393","https://openalex.org/W6774314701","https://openalex.org/W6779326418"],"related_works":["https://openalex.org/W2185469136","https://openalex.org/W2011264131","https://openalex.org/W4306353150","https://openalex.org/W2026860389","https://openalex.org/W8219677","https://openalex.org/W3216879894","https://openalex.org/W4301143707","https://openalex.org/W2952745240","https://openalex.org/W4309346246","https://openalex.org/W2565656575"],"abstract_inverted_index":{"Recently,":[0],"multi-modal":[1,52],"remote":[2],"sensing":[3],"image":[4],"(MRSI)":[5],"classification":[6,14],"has":[7],"attracted":[8],"increasing":[9],"attention":[10],"of":[11,15],"researchers.":[12],"However,":[13],"MRSI":[16,39],"with":[17],"limited":[18],"labeled":[19],"instances":[20],"is":[21,36,93],"still":[22],"a":[23,29,84],"challenging":[24],"task.":[25],"In":[26],"this":[27],"paper,":[28],"novel":[30],"self-supervised":[31],"cross-modal":[32,44,61,89],"contrastive":[33,45,62],"learning":[34,46,63],"method":[35,135],"proposed":[37,134],"for":[38,109],"classification.":[40,112],"Joint":[41],"intra-":[42,59],"and":[43,57,60,79,126,128],"are":[47,65,115],"used":[48],"to":[49,74],"better":[50,101],"mine":[51],"feature":[53],"representations":[54],"during":[55],"pre-training,":[56],"the":[58,71,96,129,133],"objectives":[64],"jointly":[66],"optimized,":[67],"whereby":[68],"it":[69],"encourages":[70],"learned":[72],"representation":[73],"be":[75],"semantically":[76],"consistent":[77],"within":[78],"between":[80],"modalities":[81,108],"simultaneously.":[82],"Moreover,":[83],"simple":[85],"but":[86],"effective":[87],"hybrid":[88],"fusion":[90],"module":[91],"(HCFM)":[92],"designed":[94],"in":[95],"fine-tuning":[97],"stage,":[98],"which":[99],"could":[100],"compactly":[102],"integrate":[103],"complementary":[104],"information":[105],"across":[106],"these":[107],"more":[110],"accurate":[111],"Extensive":[113],"experiments":[114],"taken":[116],"on":[117],"four":[118],"benchmark":[119],"datasets":[120],"(i.e.,":[121],"Houston":[122],"2013,":[123],"Augsburg,":[124],"Trento,":[125],"Berlin),":[127],"results":[130],"show":[131],"that":[132],"outperforms":[136],"state-of-the-art":[137],"methods.":[138]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":21},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
