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Identification and correction of mislabeled training data for land cover classification based on ensemble margin

In remote sensing, where training data are typically ground-based, mislabeled training data is inevitable. This work handles the mislabeling problem by exploiting the ensemble margin for identifying, then eliminating or correcting the mislabeled training data. The effectiveness of our class noise removal and correction methods is demonstrated in performing mapping of land covers. A comparative analysis is conducted with respect to the majority vote filter, a reference ensemble-based class noise filter.