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Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12590/17065
Title: A Comparison of Machine Learning Classifiers for Water-Body Segmentation Task in the PeruSAT-1 Imagery
Authors: Huauya, R.
Moreno, F.
Peña, J.
Dianderas, E.
Mauricio, A.
Díaz, J,
Keywords: Machine learning;PeruSAT-1;Water-body segmentation
Issue Date: 2021
Publisher: Springer Science and Business Media Deutschland GmbH
metadata.dc.relation.uri: https://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:1
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"
URI: http://hdl.handle.net/20.500.12590/17065
ISBN: 9783030575472
ISSN: 21903018
Appears in Collections:Artículos - Ciencia de la computación

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