Crop classification from Sentinel-2-derived vegetation indices using ensemble learning

Rei Sonobe

The two most common classification algorithms, random forests (RF) and support vector machine (SVM), were applied to conduct cropland classification from MSI data. Additionally, super learning was applied for more improvement, achieving overall accuracies of 90.2% to 92.2%. Of the two algorithms applied (RF and SVM), the accuracy of SVM was superior and 89.3% to 92.0% of overall accuracies were confirmed.




Open Atlas, how it works

Find an algorithm

Contact the author

Create a partnership

Endless opportunities