SAR Long-Tailed Target Recognition under Evidence Learning Optimization

Research about SAR target recognition has received a lot of attention in recent years. The relevant research results encountered some obstacles in their practical application. This is partly due to the long-tailed distribution of data in real-life scenarios. Specifically, a majority of data samples are concentrated in a few categories. The skewed distribution can cause learning bias toward the majority class. Although there have been some studies on long-tailed recognition for natural images, we found that these methods still need improvement when applied to SAR scenes. On the one hand, these studies use balanced datasets for performance testing, which is incompatible with imbalanced SAR target recognition. On the other hand, the predictions of these methods are unreliable for identifying high-value tail class SAR targets that are sensitive to faults. To address these issues, We propose a SAR long-tailed target recognition method based on evidence learning. The evidence learning head can output uncertainty as a measure of predictive confidence. On this basis, we demonstrate the use of uncertainty to further optimize the long-tailed SAR recognition performance and achieve optimal performance when the predicted distribution is unknown.