In this paper, we propose a novel Synthetic Minority Oversampling Technique based
Rotation forest (SMOTERoF) algorithm for the classification of imbalanced hyperspectral
image data. The main idea of the proposed method is to iteratively balance the class
distribution of training set by SMOTE for each rotation decision tree. Experiment results on
the hyperspectral image Indian Pines AVRIS with different imbalance ratio (IR) show that our
algorithm obtains better classification performance compared with Rotation Forest (RoF) …

Rotation forest (RoF) is a powerful ensemble classifier and has attracted substantial
attention due to its performance in hyperspectral data classification. Multi-class imbalance
learning is one of the biggest challenges in machine learning and remote sensing. The
standard technique for constructing RoF ensemble tends to increase the overall accuracy;
RoF has difficulty to sufficiently recognize the minority class. This paper proposes a novel
dynamic SMOTE (synthetic minority oversampling technique)-based RoF algorithm for the …