Causal Intervention and Parameter-Free Reasoning for Few-Shot SAR Target Recognition

The target recognition of synthetic aperture radar (SAR) data generally faces the issue of limited observational samples in practical applications. Recent few-shot SAR target recognition techniques based on meta-learning, which mainly focus on intricate meta-learning models without considering SAR imaging characteristics during model training, show promise. To address this issue, a novel few-shot transfer learning paradigm named causal intervention and parameter-free reasoning (CIPR) is proposed for SAR target recognition. In the proposed framework, causal intervention pretraining (CIP), which emphasizes causal features of SAR images, is developed to diminish spurious correlations caused by confounders. Moreover, variational inference approximates intricate alterations in SAR imaging angles and background clutter in a generative manner. To make predictions of the unlabelled query set without additional learnable parameters, a parameter-free label reasoning model based on optimal transport, which integrates label knowledge and effectively leverages the distribution characteristics of causal features, is introduced. Experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset demonstrate that the proposed method achieves superior performance and has preferable robustness to large depression angle discrepancies.