Title
A Comparison of Machine Learning Classifiers for Water-Body Segmentation Task in the PeruSAT-1 Imagery
Date Issued
01 January 2021
Resource Type
Book Series
Author(s)
Huauya R.
Moreno F.
Peña J.
Dianderas E.
Mauricio A.
Díaz J.
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.
Start page
69
End page
78
Volume
201
Scopus EID
2-s2.0-85098184611
ISBN
9783030575472
Source
Smart Innovation, Systems and Technologies
Resource of which it is part
Smart Innovation, Systems and Technologies
ISSN of the container
21903018
Source funding
Universidad Nacional de Ingenierias
Sources of information: Scopus Directorio de Producción Científica