Performance evaluation of sentinel-2 and landsat 8 OLI data for land cover/use classification using a comparison between machine learning algorithms

• 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/id/eprint/95756/1/AnuarAhmad2021_PerformanceEvaluationofSentinel2andLandsat8.pdf; http://eprints.utm.my/id/eprint/95756/; http://dx.doi.org/10.3390/rs13071349

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...