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dc.contributor.authorZhang, Tianxiang
dc.contributor.authorSu, Jinya
dc.contributor.authorXu, Zhiyong
dc.contributor.authorLuo, Yulin
dc.contributor.authorLi, Jiangyun
dc.date.accessioned2022-04-25T11:25:01Z
dc.date.available2022-04-25T11:25:01Z
dc.date.issued2021-01-08
dc.identifier.citationZhang , T , Su , J , Xu , Z , Luo , Y & Li , J 2021 , ' Sentinel-2 satellite imagery for urban land cover classification by optimized random forest classifier ' , Applied Sciences (Switzerland) , vol. 11 , no. 2 , 543 . https://doi.org/10.3390/app11020543en
dc.identifier.issn2076-3417
dc.identifier.otherPURE: 215299756
dc.identifier.otherPURE UUID: 1a95698a-0da0-4430-aa41-60152b16832d
dc.identifier.otherScopus: 85099230620
dc.identifier.otherORCID: /0000-0002-3121-7208/work/112496158
dc.identifier.urihttps://hdl.handle.net/2164/18476
dc.descriptionFunding: This work was supported by the Fundamental Research Funds for the China Central Universities of USTB (FRF-DF-19-002), Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB (BK20BE014).en
dc.format.extent17
dc.language.isoeng
dc.relation.ispartofApplied Sciences (Switzerland)en
dc.rightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/)en
dc.subjectSDG 15 - Life on Landen
dc.subjectBayesian optimizationen
dc.subjectHyperparameter tuningen
dc.subjectLand cover classificationen
dc.subjectRandom foresten
dc.subjectSentinel-2 satelliteen
dc.subjectUrban managementen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectMaterials Science(all)en
dc.subjectInstrumentationen
dc.subjectEngineering(all)en
dc.subjectProcess Chemistry and Technologyen
dc.subjectComputer Science Applicationsen
dc.subjectFluid Flow and Transfer Processesen
dc.subject.lccQA75en
dc.titleSentinel-2 satellite imagery for urban land cover classification by optimized random forest classifieren
dc.typeJournal articleen
dc.contributor.institutionUniversity of Aberdeen.Computing Scienceen
dc.contributor.institutionUniversity of Aberdeen.Machine Learningen
dc.description.statusPeer revieweden
dc.description.versionPublisher PDFen
dc.identifier.doihttps://doi.org/10.3390/app11020543
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85099230620&partnerID=8YFLogxKen


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