A Robust Dynamic Classifier Selection Approach for Hyperspectral Images with Imprecise Label Information.

Supervised hyperspectral image (HSI) classification relies on accurate label information. However, it is not always possible to collect perfectly accurate labels for training samples. This motivates the development of classifiers that are sufficiently robust to some reasonable amounts of errors in data labels.