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: Ghayour, Laleh, Neshat, Aminreza, Paryani, Sina, Shahabi, Himan, Shirzadi, Ataollah, Chen, Wei, Al-Ansari, Nadhir, Geertsema, Marten, Amiri, Mehdi Pourmehdi, Gholamnia, Mehdi, Dou, Jie, Ahmad, Anuar
Format: Article
Language:English
Published: MDPI 2021
Subjects:
G70.39-70.6 Remote sensing
Online Access:http://eprints.utm.my/95756/1/AnuarAhmad2021_PerformanceEvaluationofSentinel2andLandsat8.pdf

Internet

http://eprints.utm.my/95756/1/AnuarAhmad2021_PerformanceEvaluationofSentinel2andLandsat8.pdf

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