A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions

A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions

Distribution of Land Cover (LC) classes is mostly imbalanced with some majority LC classes dominating against minority classes in mountainous areas. Although standard Machine Learning (ML) classifiers can achieve high accuracies for majority classes, they largely fail to provide reasonable accuracie...

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Bibliographic Details
Main Authors: Amin Naboureh, Ainong Li, Jinhu Bian, Guangbin Lei, Meisam Amani
Format: Article
Language:English
Published: MDPI AG 2020-10-01
Series:Remote Sensing
Subjects:
class imbalance problem
Google Earth Engine
land cover mapping
mountainous regions
time-series of Landsat
Online Access:https://www.mdpi.com/2072-4292/12/20/3301

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https://www.mdpi.com/2072-4292/12/20/3301

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