Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers

Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers

Atmospheric correction is one of the key parts of remote sensing preprocessing because it can influence and change the final classification result. This research examines the impact of five different atmospheric correction processing on land cover classification accuracy using Sentinel-2 satellite i...

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Bibliographic Details
Main Authors: Luka Rumora, Mario Miler, Damir Medak
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:ISPRS International Journal of Geo-Information
Subjects:
atmospheric correction
Sentinel-2
land cover classification
machine learning
radiometric indices
SVM
Online Access:https://www.mdpi.com/2220-9964/9/4/277

Internet

https://www.mdpi.com/2220-9964/9/4/277

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