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The supervised classification of satellite image time series allows obtaining reliable land cover maps over large areas. However, their quality depends on the reference datasets used for training the classifier. In remote sensing, reference data may lack of timeliness and accuracy which leads to the presence of mislabeled data degrading the classification performances. This work presents an iterative...
The supervised classification of optical image time series allow the production of accurate land cover maps over large areas. However, the precision yielded by learning algorithms strongly depends on the quality of the reference data. The reference databases covering a large geographical area usually contain noisy data with an important number of mislabeled instances. These labeling errors result...
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