Single-Domain Generalization via Multilevel Data Augmentation for SAR Target Recognition Training on Fully Simulated Data

Due to the existence of domain shift, the synthetic aperture radar (SAR) automatic target recognition (ATR) model trained on simulated data will have significant performance degradation when applied to real-world measured data. To bridge the domain gap, this paper proposes a single-domain generalization (SDG) method based on multilevel data augmentation (MLDA), enabling SAR-ATR models that have been fully trained on simulated data to be generalized to unseen real SAR data. The proposed method aims to enhance the model’s generalizable capability through three key components: (1) pixel-level augmentation, which enriches data distribution via random Gaussian noise injection in the spatial domain and high-frequency perturbation in the frequency domain to enhance pixel-level diversity; (2) feature-level style augmentation, which probabilistically mixes instance-wise feature statistics, generating hybrid-styled feature maps to enhance feature-level diversity; (3) domain-adversarial training, which constructs an adversarial framework between a feature extractor and discriminator to enforce the learning of domain-invariant representations. Experiments on two simulation-to-reality SAR datasets demonstrate that the proposed method outperforms existing baselines and other SDG algorithms, achieving state-of-the-art (SOTA) performance on both datasets (96.76% accuracy on the public SAMPLE dataset and 93.70% accuracy on the self-built S2M-5 dataset).