Adaptive Cost Adjustment for SAR Imbalanced Classification via Reinforcement Learning

Synthetic aperture radar (SAR) images are difficult to acquire, and the number of images of different targets often varies greatly, resulting in a large number of imbalanced class distributions in practical applications. Existing classification models usually focus too much on the classes with a large number of samples and less on the minority classes with higher-value, which leads to the degradation of classification performance. A novel method for imbalanced classification in SAR images based on reinforcement learning adaptive cost adjustment is proposed in this paper. The method can adaptively search a cost factor and adjust it continuously according to the predicted classification effect so that the performance of all classes of samples is the best possible. This method can obtain a more accurate cost factor and modify the decision boundary of the classifier to achieve a more accurate classification of the minority classes. Experiments on the MSTAR dataset show that the proposed method achieves binary and multi-classification, significantly alleviates the effect of imbalanced data, shows better classification results on all kinds of samples, and outperforms existing methods in overall performance.