A Two-Stage Causal Intervention Framework for Long-Tailed SAR Target Recognition

The distribution of SAR targets generally conforms to a long-tailed distribution. Due to the existence of sample distribution bias and sample selection bias, training classifiers on this distribution of data often introduces spurious correlations between samples and classes. To address this issue, we propose a two-stage causal intervention framework. The core is that structural causality allows for independent interventions on multiple biases, thereby ensuring high-quality tail class predictions while maintaining unbiased performance for head classes. Firstly, we construct a structural causal graph for the long-tailed recognition task from causal perspective. Based on this graph, the causal paths underlying the two types of biases are identified. Secondly, we design a data augmentation method named DiagPatch-M, which identifies causal features within samples. In this process, these generated patches randomly integrate causal and non-causal features from two different samples, disrupting the original recognition process and effectively eliminating biases induced by sample selection. Thirdly, we design an unbiased structural risk minimization (USRM) optimization strategy, which eliminates the “head preference” of conventional models and the “tail preference” of modified models. This strategy reduces the bias introduced by the model’s dependence on the original sample distribution, and achieves stable recognition under different sample distributions. Experimental results on two long-tailed and two balanced datasets demonstrate that the effectiveness of our model surpasses the state-of-the-art (SOTA) methods, indicating the efficacy of our proposed framework in tackling the challenges posed by the long-tailed distribution in SAR target recognition.