Title
A Comparison of Machine Learning Classifiers for Water-Body Segmentation Task in the PeruSAT-1 Imagery
Date Issued
01 January 2021
Access level
metadata only access
Resource Type
conference paper
Author(s)
Universidad Nacional de Ingeniería
Instituto de Investigaciones de la Amazonía Peruana
Universidad Nacional de Ingeniería
Publisher(s)
Springer Science and Business Media Deutschland GmbH
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
Language
English
OCDE Knowledge area
Ciencias de la computación
Ingeniería ambiental y geológica
Sensores remotos
Subjects
Scopus EID
2-s2.0-85098184611
Source
Smart Innovation, Systems and Technologies
Resource of which it is part
Smart Innovation, Systems and Technologies
ISSN of the container
21903018
ISBN of the container
9783030575472
Conference
Smart Innovation, Systems and Technologies
Sponsor(s)
The present work was supported by the Vice rectorate for Research of the Universidad Nacional de Ingeniería (VRI–UNI). The satellite imagery was provided thankfully by the Head Space Agency of Peru (CONIDA).
Sources of information:
Directorio de Producción Científica
Scopus