Improving Automatic Target Recognition With Infrared Imagery Using Vision Transformers and Focused Data Augmentation

We propose a data augmentation strategy to improve Automatic Target Recognition (ATR) from Infrared (IR) imagery using vision transformers. Our method leverages external IR image repositories to select relevant samples that can boost the diversity of the training data. By doing so, we improve the model ability to learn from the challenging regions of the training feature space. Our approach uses attention-based explanations to identify under-represented regions in the feature space of the training data. We leverage this information to search for new samples that complement the current training data by covering the sparse gaps in the feature space. We evaluate the proposed approach on a public dataset with IR imagery of multiple targets. We show that our method achieves a significant 2% improvement in ATR performance compared to a baseline model trained without augmentation. We also show that our method outperforms other data augmentation techniques that do not consider under-represented regions. These results demonstrate the effectiveness of our approach in an infrared scenario, where there is high intraclass variance and large training sets are expensive to obtain.