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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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