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Objective: Rotation Forest (RoF), a powerful ensemble classifier, has obtained many successful applications in hyperspectral image classification. However, the data often has the problem of class imbalance, which makes the traditional RoF algorithm focus on identifying samples of most classes, while ignoring the accuracy of minority samples. The SMOTE algorithm increases the number of minority samples by simulating the way of generating new samples, thereby achieving the effect of balancing the categories of the data set. However, the SMOTE algorithm is mainly used in the data preprocessing stage, and has the risk of increasing artificial noise when dealing with multi-class problems. Therefore, to increase the classification accuracy of the multi-class imbalanced hyperspectral data, a novel dynamic ensemble algorithm based on SMOTE and RoF is proposed in this paper. Method: The proposed algorithm uses dynamic sampling factor technology to merge the class distribution optimization with the base classifier. This algorithm can not only realize the adaptive generation of class balance data set, but also greatly reduce the influence of noise on the base classifier. Result: In this experiment, three public hyperspectral images are used to test the performance of the algorithm, four comparison algorithms are selected, including random forest, traditional RoF as well as RoF algorithm with random oversampling and SMOTE data preprocessing respectively. Overall accuracy, average accuracy, F-measure, Gmean, minimum recall rate, ensemble classifier diversity, model training time and McNemar test are the algorithm evaluation criteria. Conclusion: The experimental results demonstrate the effectiveness of the proposed method. The novel method not only has obvious classification advantages, but also can increase the recognition accuracy of minority samples while keeping the overall classification accuracy of the data.