Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms

Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms

With the development of remote sensing algorithms and increased access to satellite data, generating up-to-date, accurate land use/land cover (LULC) maps has become increasingly feasible for evaluating and managing changes in land cover as created by changes to ecosystem and land use. The main objec...

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Bibliographic Details
Main Authors: Laleh Ghayour, Aminreza Neshat, Sina Paryani, Himan Shahabi, Ataollah Shirzadi, Wei Chen, Nadhir Al-Ansari, Marten Geertsema, Mehdi Pourmehdi Amiri, Mehdi Gholamnia, Jie Dou, Anuar Ahmad
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Remote Sensing
Subjects:
land cover
machine learning
remote sensing
satellite imagery
classification accuracy
Saqqez
Online Access:https://www.mdpi.com/2072-4292/13/7/1349

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