Improving land cover classification accuracy of Sentinel-2 images: a systematic review of articles between 2015 and 2021
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12517-024-11945-0.pdf
Reference51 articles.
1. Abdi AM (2020) Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. Gisci Remote Sens 57(1):1–20
2. Adiri Z, Lhissou R, El Harti A, Jellouli A, Chakouri M (2020) Recent advances in the use of public domain satellite imagery for mineral exploration: a review of Landsat-8 and Sentinel-2 applications. Ore Geol Rev 117:103332. https://doi.org/10.1016/j.oregeorev.2020.103332
3. Andrew ME, Wulder MA, Nelson TA (2014) Potential contributions of remote sensing to ecosystem service assessments. Prog Phys Geogr 38(3):328–353. https://doi.org/10.1177/0309133314528942
4. Belgiu M, Drăguţ L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm 114:24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
5. Chan JC-W, Beckers P, Spanhove T, Borre JV (2012) An evaluation of ensemble classifiers for mapping Natura 2000 heathland in Belgium using spaceborne angular hyperspectral (CHRIS/Proba) imagery. Int J Appl Earth Obs 18:13–22. https://doi.org/10.1016/j.jag.2012.01.002
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