{"id":"https://openalex.org/W4414925100","doi":"https://doi.org/10.3389/fcomp.2025.1613648","title":"Improving remote sensing scene classification with data augmentation techniques to mitigate class imbalance","display_name":"Improving remote sensing scene classification with data augmentation techniques to mitigate class imbalance","publication_year":2025,"publication_date":"2025-10-08","ids":{"openalex":"https://openalex.org/W4414925100","doi":"https://doi.org/10.3389/fcomp.2025.1613648"},"language":"en","primary_location":{"id":"doi:10.3389/fcomp.2025.1613648","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fcomp.2025.1613648","pdf_url":"https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1613648/pdf","source":{"id":"https://openalex.org/S4210211086","display_name":"Frontiers in Computer Science","issn_l":"2624-9898","issn":["2624-9898"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Computer Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1613648/pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100338726","display_name":"Ping Wang","orcid":"https://orcid.org/0000-0003-2932-0617"},"institutions":[{"id":"https://openalex.org/I4210155952","display_name":"Qingdao Huanghai University","ror":"https://ror.org/05e1zbn94","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210155952"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Wang","raw_affiliation_strings":["Qingdao Huanghai University, Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Qingdao Huanghai University, Qingdao, China","institution_ids":["https://openalex.org/I4210155952"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089801324","display_name":"Xin Zhao","orcid":"https://orcid.org/0000-0002-8937-1769"},"institutions":[{"id":"https://openalex.org/I4210155952","display_name":"Qingdao Huanghai University","ror":"https://ror.org/05e1zbn94","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210155952"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Zhao","raw_affiliation_strings":["Qingdao Huanghai University, Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Qingdao Huanghai University, Qingdao, China","institution_ids":["https://openalex.org/I4210155952"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034422253","display_name":"Yuanhui Chen","orcid":"https://orcid.org/0000-0003-4408-7016"},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanhui Chen","raw_affiliation_strings":["College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China","institution_ids":["https://openalex.org/I80143920"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060427609","display_name":"Lili Zhan","orcid":null},"institutions":[{"id":"https://openalex.org/I80143920","display_name":"Shandong University of Science and Technology","ror":"https://ror.org/04gtjhw98","country_code":"CN","type":"education","lineage":["https://openalex.org/I80143920"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lili Zhan","raw_affiliation_strings":["College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, China","institution_ids":["https://openalex.org/I80143920"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5060427609"],"corresponding_institution_ids":["https://openalex.org/I80143920"],"apc_list":{"value":1150,"currency":"USD","value_usd":1150},"apc_paid":{"value":1150,"currency":"USD","value_usd":1150},"fwci":4.188,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.94362387,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"7","issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9919000267982483,"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"}},"topics":[{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9919000267982483,"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"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9796000123023987,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9781000018119812,"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/class","display_name":"Class (philosophy)","score":0.59170001745224},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.48089998960494995},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.44200000166893005},{"id":"https://openalex.org/keywords/cohens-kappa","display_name":"Cohen's kappa","score":0.3944999873638153},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.3806000053882599},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3711000084877014}],"concepts":[{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6588000059127808},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5997999906539917},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.59170001745224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5365999937057495},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.48089998960494995},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.44200000166893005},{"id":"https://openalex.org/C163864269","wikidata":"https://www.wikidata.org/wiki/Q1107106","display_name":"Cohen's kappa","level":2,"score":0.3944999873638153},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.3806000053882599},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3711000084877014},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.3366999924182892},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.32919999957084656},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30169999599456787},{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.29490000009536743},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28279998898506165},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.27090001106262207},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.25119999051094055}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3389/fcomp.2025.1613648","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fcomp.2025.1613648","pdf_url":"https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1613648/pdf","source":{"id":"https://openalex.org/S4210211086","display_name":"Frontiers in Computer Science","issn_l":"2624-9898","issn":["2624-9898"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Computer Science","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:55452c2edf6e4ff4bcc0e6e4ce678e60","is_oa":true,"landing_page_url":"https://doaj.org/article/55452c2edf6e4ff4bcc0e6e4ce678e60","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Frontiers in Computer Science, Vol 7 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3389/fcomp.2025.1613648","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fcomp.2025.1613648","pdf_url":"https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1613648/pdf","source":{"id":"https://openalex.org/S4210211086","display_name":"Frontiers in Computer Science","issn_l":"2624-9898","issn":["2624-9898"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Computer Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7058350159","display_name":"DEM\u7eb9\u7406\u7684\u5730\u7406\u7a7a\u95f4\u8ba4\u77e5\u4e0e\u5206\u6790\u7814\u7a76","funder_award_id":"41971339","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"},{"id":"https://openalex.org/F4320335661","display_name":"State Key Laboratory of Synthetical Automation for Process Industries","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4414925100.pdf","grobid_xml":"https://content.openalex.org/works/W4414925100.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W1502624642","https://openalex.org/W2097117768","https://openalex.org/W2321627895","https://openalex.org/W2549139847","https://openalex.org/W2592962403","https://openalex.org/W2766296277","https://openalex.org/W2767106145","https://openalex.org/W2790979755","https://openalex.org/W2793305583","https://openalex.org/W2800788706","https://openalex.org/W2890833618","https://openalex.org/W2899434936","https://openalex.org/W2912089046","https://openalex.org/W2918408501","https://openalex.org/W2936503027","https://openalex.org/W2945385604","https://openalex.org/W2946655868","https://openalex.org/W2963420686","https://openalex.org/W2974373385","https://openalex.org/W2982083293","https://openalex.org/W2989219518","https://openalex.org/W2990903938","https://openalex.org/W2993620107","https://openalex.org/W3014785587","https://openalex.org/W3022140654","https://openalex.org/W3097090744","https://openalex.org/W3117704549","https://openalex.org/W3119125170","https://openalex.org/W3130213261","https://openalex.org/W3172075653","https://openalex.org/W4232204491","https://openalex.org/W4255556797","https://openalex.org/W4299518610","https://openalex.org/W4389961071","https://openalex.org/W4391974596","https://openalex.org/W4400680087","https://openalex.org/W4405598539"],"related_works":[],"abstract_inverted_index":{"High-resolution":[0],"remote":[1,46],"sensing":[2,47],"imagery":[3],"is":[4,30],"a":[5,106],"powerful":[6],"tool":[7],"that":[8,35,174],"provides":[9],"massive":[10],"information":[11],"about":[12],"ground":[13],"objects.":[14],"However,":[15],"conventional":[16,36],"methods":[17,37],"often":[18],"fail":[19],"to":[20,32,40,68,149,203],"achieve":[21],"satisfactory":[22],"results":[23,172],"for":[24,83,190],"complex":[25],"urban":[26],"scene":[27,49],"classification.":[28],"This":[29,87],"attributed":[31],"the":[33,42,74,90,98,103,144,175,186,191,209,219],"fact":[34],"are":[38,53],"unable":[39],"meet":[41],"requirements":[43],"of":[44,92,102],"high-accuracy":[45],"image":[48],"classification":[50,69,94,176,216],"(RSSC)":[51],"and":[52,62,137,159,163,168,198,205,214,222,229,238],"hindered":[54],"by":[55,96,179,227],"challenges":[56],"such":[57],"as":[58],"limited":[59],"labeled":[60,116],"samples":[61],"class":[63,180],"imbalance,":[64],"which":[65],"may":[66],"lead":[67],"bias":[70,95,177],"in":[71,236],"classifiers.":[72],"On":[73],"contrary,":[75],"deep":[76],"learning-based":[77],"RSSC":[78],"represents":[79],"an":[80],"important":[81],"approach":[82],"understanding":[84],"semantic":[85],"information.":[86],"paper":[88],"explores":[89],"feasibility":[91],"mitigating":[93],"reducing":[97,143,215],"imbalance":[99,181],"ratio":[100],"(IR)":[101],"dataset.":[104],"First,":[105],"class-imbalanced":[107,169],"dataset":[108],"was":[109,182],"constructed":[110],"using":[111],"very":[112],"high-resolution":[113],"(VHR)":[114],"images,":[115],"into":[117],"nine":[118],"land":[119],"use/land":[120],"cover":[121],"(LULC)":[122],"categories.":[123],"Second,":[124],"comprehensive":[125],"data":[126],"augmentation":[127],"techniques":[128],"(mirroring,":[129],"rotation,":[130],"cropping,":[131],"Hue,":[132],"Saturation,":[133],"Value":[134],"(HSV)":[135],"perturbation,":[136],"gamma":[138],"transformation)":[139],"were":[140,161],"applied,":[141],"successfully":[142],"dataset's":[145],"IR":[146],"from":[147],"9.38":[148],"1.28.":[150],"Subsequently,":[151],"four":[152],"architectures,":[153],"MobileNet-v2,":[154],"ResNet101,":[155],"ResNeXt101_32":[156],"\u00d7":[157],"32d,":[158],"Transformer,":[160],"trained":[162],"evaluated":[164],"on":[165],"both":[166],"class-balanced":[167],"datasets.":[170],"The":[171],"indicate":[173],"caused":[178],"alleviated,":[183],"significantly":[184],"improving":[185],"classifier's":[187],"performance.":[188],"Specifically":[189],"most":[192],"severely":[193],"underrepresented":[194],"category":[195],"(intersections),":[196],"precision":[197],"recall":[199,237],"improvements":[200],"reached":[201],"up":[202],"128%":[204],"102%,":[206],"respectively,":[207,231],"narrowing":[208],"gap":[210],"with":[211,232],"other":[212],"categories":[213],"bias.":[217],"Furthermore,":[218],"average":[220],"Kappa":[221],"overall":[223],"accuracy":[224],"(OA)":[225],"increased":[226],"11.84%":[228],"12.97%,":[230],"reduced":[233],"standard":[234],"deviations":[235],"precision,":[239],"demonstrating":[240],"enhanced":[241],"model":[242],"stability.":[243]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}