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...

Full description

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

Similar Items

Similar Items