Imbalanced Hyperspectral Image Classification With an Adaptive Ensemble Method Based on SMOTE and Rotation Forest With Differentiated Sampling Rates

Rotation forest (RoF) is a powerful ensemble classifier and has been demonstrated the outstanding performance in hyperspectral data classification. However, the classification task suffers from the class imbalanced problem which has been considered to be one of the most important challenges. The traditional construction method of RoF biases classifying the majority classes and ignores recognizing the minority classes samples. This letter proposes a novel adaptive ensemble method based on SMOTE and RoF with differentiated sampling rates (AdaSRoF) for the multiclass imbalance problem. The proposed method adaptively generates several balanced data sets with more diversity and less noise by using SMOTE and a dynamic data sampling ratio for base classifiers. The obtained results on two publicly available hyperspectral images show that the proposed method can get more diversity and better performance than support vector machine (SVM), random forest (RF), and RoF in multiclass imbalance learning.