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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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 |
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Format: | Article |
Language: | English |
Published: |
MDPI
2021
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Subjects: | |
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 |
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