{"id":"https://openalex.org/W4404172105","doi":"https://doi.org/10.1145/3697467.3697646","title":"A Remote Sensing Image Classification Based on Contrast Learning with Similarity Fusion","display_name":"A Remote Sensing Image Classification Based on Contrast Learning with Similarity Fusion","publication_year":2024,"publication_date":"2024-08-09","ids":{"openalex":"https://openalex.org/W4404172105","doi":"https://doi.org/10.1145/3697467.3697646"},"language":"en","primary_location":{"id":"doi:10.1145/3697467.3697646","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3697467.3697646","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 4th International Conference on Internet of Things and Machine Learning","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/A5111191751","display_name":"Yan Zeng","orcid":null},"institutions":[{"id":"https://openalex.org/I927504317","display_name":"Nanchang Hangkong University","ror":"https://ror.org/0369pvp92","country_code":"CN","type":"education","lineage":["https://openalex.org/I927504317"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Zeng","raw_affiliation_strings":["School of Software, Nanchang Hangkong University, Nanchang, Jiangxi, China"],"raw_orcid":"https://orcid.org/0009-0005-7933-7080","affiliations":[{"raw_affiliation_string":"School of Software, Nanchang Hangkong University, Nanchang, Jiangxi, China","institution_ids":["https://openalex.org/I927504317"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Hongmei Li","orcid":"https://orcid.org/0009-0004-6213-6613"},"institutions":[{"id":"https://openalex.org/I4210163048","display_name":"Jiangxi College of Applied Technology","ror":"https://ror.org/05d5kcc69","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210163048"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongmei Li","raw_affiliation_strings":["School of Art Media and Computer, Jiangxi Tourism and Trade Vocational College, Nanchang, Jiangxi, China"],"raw_orcid":"https://orcid.org/0009-0004-6213-6613","affiliations":[{"raw_affiliation_string":"School of Art Media and Computer, Jiangxi Tourism and Trade Vocational College, Nanchang, Jiangxi, China","institution_ids":["https://openalex.org/I4210163048"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Sijia Wang","orcid":"https://orcid.org/0009-0004-7814-217X"},"institutions":[{"id":"https://openalex.org/I927504317","display_name":"Nanchang Hangkong University","ror":"https://ror.org/0369pvp92","country_code":"CN","type":"education","lineage":["https://openalex.org/I927504317"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sijia Wang","raw_affiliation_strings":["School of Software, Nanchang Hangkong University, Nanchang, Jiangxi, China"],"raw_orcid":"https://orcid.org/0009-0004-7814-217X","affiliations":[{"raw_affiliation_string":"School of Software, Nanchang Hangkong University, Nanchang, Jiangxi, China","institution_ids":["https://openalex.org/I927504317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045120688","display_name":"Yun Ge","orcid":null},"institutions":[{"id":"https://openalex.org/I927504317","display_name":"Nanchang Hangkong University","ror":"https://ror.org/0369pvp92","country_code":"CN","type":"education","lineage":["https://openalex.org/I927504317"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yun Ge","raw_affiliation_strings":["School of Software, Nanchang Hangkong University, Nanchang, Jiangxi, China"],"raw_orcid":"https://orcid.org/0000-0003-2693-965X","affiliations":[{"raw_affiliation_string":"School of Software, Nanchang Hangkong University, Nanchang, Jiangxi, China","institution_ids":["https://openalex.org/I927504317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"222","last_page":"228"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9959999918937683,"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":0.9959999918937683,"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":0.9918000102043152,"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/T10057","display_name":"Face and Expression Recognition","score":0.9710000157356262,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7093636989593506},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.6664484739303589},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6421618461608887},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6398681402206421},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6037011742591858},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5827118158340454},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.5721663236618042},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5349684357643127},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4034273028373718},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.39924904704093933}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7093636989593506},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.6664484739303589},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6421618461608887},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6398681402206421},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6037011742591858},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5827118158340454},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.5721663236618042},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5349684357643127},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4034273028373718},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.39924904704093933},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3697467.3697646","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3697467.3697646","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2024 4th International Conference on Internet of Things and Machine Learning","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2291467501","https://openalex.org/W2762941833","https://openalex.org/W2974373385","https://openalex.org/W4298146258","https://openalex.org/W4366377626","https://openalex.org/W4389352512","https://openalex.org/W4392309149"],"related_works":["https://openalex.org/W2788731446","https://openalex.org/W2204403038","https://openalex.org/W3152170969","https://openalex.org/W2379054866","https://openalex.org/W2549658594","https://openalex.org/W2370195708","https://openalex.org/W1490651872","https://openalex.org/W2139242969","https://openalex.org/W2284201331","https://openalex.org/W2095903272"],"abstract_inverted_index":{"For":[0],"the":[1,19,39,45,64,81,93,99,106,118,156,164],"problem":[2],"that":[3,121,155],"conventional":[4],"contrastive":[5,107,132],"loss":[6,108],"functions":[7],"employ":[8],"a":[9,74,126,147],"single":[10,127],"similarity":[11,28,54,95,128,160,165],"measure,":[12],"which":[13],"result":[14],"in":[15,55,143,175],"features":[16,97],"inadequately":[17],"represent":[18],"complex":[20],"content":[21],"of":[22,42,48,96,140,150,158],"remote":[23,84,111,177],"sensing":[24,85,112,178],"images.":[25],"A":[26],"fusion":[27,76],"method":[29],"based":[30],"on":[31],"self-supervised":[32],"contrast":[33],"learning":[34],"is":[35],"proposed":[36],"to":[37,67,123,171],"improve":[38,172],"discriminative":[40],"ability":[41],"features.":[43],"Given":[44],"significant":[46],"advantages":[47],"Pearson's":[49],"correlation":[50],"coefficient":[51],"and":[52,88,98],"cosine":[53],"helping":[56],"models":[57],"learn":[58],"patterns":[59],"between":[60,83,166],"images":[61,86],"more":[62,168],"robustly,":[63],"paper":[65],"proposes":[66],"integrate":[68],"these":[69],"two":[70],"metrics":[71],"by":[72,90],"constructing":[73],"weighted":[75],"mechanism.":[77],"Our":[78],"approach":[79,135],"quantifies":[80],"relationships":[82],"comprehensively":[87],"multi-dimensionally,":[89],"considering":[91],"both":[92],"directional":[94],"linear":[100],"correlations":[101],"among":[102],"them,":[103],"thus":[104],"refining":[105],"function.":[109],"Three":[110],"datasets":[113],"are":[114],"selected":[115],"for":[116],"experiments,":[117],"results":[119],"reveal":[120],"compare":[122],"methods":[124],"employing":[125],"metric":[129],"combined":[130],"with":[131,146],"loss,":[133],"our":[134],"achieves":[136],"an":[137],"average":[138],"improvement":[139],"approximately":[141],"1.6%":[142],"classification":[144,173],"accuracy,":[145],"peak":[148],"enhancement":[149],"about":[151],"2.8%.":[152],"This":[153],"validates":[154],"strategy":[157],"fusing":[159],"measure":[161],"can":[162],"capture":[163],"samples":[167],"extensively,":[169],"contributing":[170],"precision":[174],"unlabeled":[176],"data.":[179]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-19T07:52:34.831488","created_date":"2025-10-10T00:00:00"}