Improving ocean chlorophyll estimation in satellite hyperspectral images using ensemble machine learning

dc.contributor.author	Flores-Romero, Alvaro
dc.date.accessioned	2023-12-21T12:49:22Z
dc.date.available	2023-12-21
dc.date.issued	2023-12-21
dc.identifier.uri	https://erepo.uef.fi/handle/123456789/31123
dc.language.iso	en
dc.publisher	Itä-Suomen yliopisto	fi
dc.publisher	University of Eastern Finland	en
dc.title	Improving ocean chlorophyll estimation in satellite hyperspectral images using ensemble machine learning
dc.contributor.department	Tietojenkäsittelytieteen laitos	fi
dc.contributor.department	School of Computing	en
dc.type.publication	info:eu-repo/semantics/masterThesis
dc.contributor.organization	Luonnontieteiden ja metsätieteiden tiedekunta, Tietojenkäsittelytieteen laitos	fi
dc.contributor.organization	Faculty of Science and Forestry, School of Computing	en
dc.format.content	fulltext
dc.identifier.urn	http://urn.fi/urn:nbn:fi:uef-20231596
dc.publisher.country	fi
dc.rights.accesslevel	openAccess
dc.type.ontasot	Pro gradu	fi
dc.type.ontasot	Master's thesis	en
uef.publication.id	22099
dc.contributor.faculty	Luonnontieteiden ja metsätieteiden tiedekunta	fi
dc.contributor.faculty	Faculty of Science and Forestry	en
dc.format.displayContent	Julkaisun kokoteksti	fi
dc.format.displayContent	Fulltext of publication	en
dc.subject.discipline	tietojenkäsittelytiede	fi
dc.subject.discipline	computer science	en
dc.type.coar	http://purl.org/eprint/type/Thesis
dc.type.displayType	Pro gradu	fi
dc.type.displayType	Masters thesis	en