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Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12590/17065
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dc.contributor.authorHuauya, R.-
dc.contributor.authorMoreno, F.-
dc.contributor.authorPeña, J.-
dc.contributor.authorDianderas, E.-
dc.contributor.authorMauricio, A.-
dc.contributor.authorDíaz, J,-
dc.date.accessioned2022-03-10T23:36:19Z-
dc.date.available2022-03-10T23:36:19Z-
dc.date.issued2021-
dc.identifier.isbn9783030575472es_PE
dc.identifier.issn21903018-
dc.identifier.urihttp://hdl.handle.net/20.500.12590/17065-
dc.description.abstract"Water-body segmentation is a high-relevance task inside satellite image analysis due to its relationship with environmental monitoring and assessment. Thereon, several authors have proposed different approaches which achieve a wide range of results depending on their datasets and settings. This study is a brief review of classical segmentation techniques in multispectral images using the Peruvian satellite PeruSAT-1 imagery. The areas of interest are medium-sized highland zones with water bodies around in Peruvian south. We aim to analyze classical segmentation methods to prevent future natural disasters, like alluviums or droughts, under low-cost data constraints. We consider accuracy, robustness, conditions, and visual effects in our analysis"es_PE
dc.description.uriTrabajo académicoes_PE
dc.language.isoenges_PE
dc.publisherSpringer Science and Business Media Deutschland GmbHes_PE
dc.relationinfo:eu-repo/semantics/articlees_PE
dc.relation.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85098184611&origin=resultslist&sort=plf-f&src=s&nlo=&nlr=&nls=&sid=c0147ee94c46e56e76c75f54bcad6ea5&sot=aff&sdt=cl&cluster=scopubyr%2c%222021%22%2ct&sl=48&s=AF-ID%28%22Universidad+Cat%c3%b3lica+San+Pablo%22+60105300%29&relpos=69&citeCnt=0&searchTerm=&featureToggles=FEATURE_NEW_DOC_DETAILS_EXPORT:1es_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.subjectMachine learninges_PE
dc.subjectPeruSAT-1es_PE
dc.subjectWater-body segmentationes_PE
dc.titleA Comparison of Machine Learning Classifiers for Water-Body Segmentation Task in the PeruSAT-1 Imageryes_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
thesis.degree.grantorUniversidad Católica San Pablo. Departamento de Ciencia de la Computaciónes_PE
thesis.degree.disciplineCiencia de la Computaciónes_PE
dc.identifier.doi10.1007/978-3-030-57548-9_6es_PE
dc.publisher.countryPEes_PE
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#1.02.02es_PE
renati.typehttps://purl.org/pe-repo/renati/type#trabajoAcademicoes_PE
Appears in Collections:Artículos - Ciencia de la computación

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