Universal Domain Adaptation for Simulation-Assisted SAR Target Recognition Based on Adversarial Uncertainty and Neighbor Relation

Fei Gao, Weiru Ma, Changshun Yuan, Jun Wang, Jinping Sun, Amir Hussain

Abstract: In the field of synthetic aperture radar (SAR) target recognition, leveraging computer-generated labeled simulated SAR data to aid in the recognition of unlabeled real SAR data has garnered significant attention. However, the domain shift between the simulated and real domain contravenes the assumption of independent and identically distributed in deep learning (DL). Moreover, the categories of real SAR data may not align precisely with those of the simulated SAR data, and the prior knowledge of the label shift typically remains unavailable. To align the common category samples and identify target-private unknown category samples in the presence of both domain shift and potential label shift, we introduce a two-stage universal domain adaptation method for simulation-assisted SAR target recognition based on adversarial uncertainty and neighbor relation (AUNR). The proposed AUNR introduces Dirichlet distribution-based evidential DL, which not only performs classification tasks but also characterizes decision uncertainty, enabling the model to express the concept of “Unknow.” In the first stage, alongside supervised learning, we leverage data augmentation-based contrastive learning to achieve robust feature representation. In the second stage, adversarial learning based on model and data uncertainty is employed to separate common and unknown class samples, and contrastive learning based on neighbor relation aligns samples in two domains according to their geometric relationships. To the best of our knowledge, this is the first study on UniDA for SAR target recognition. Experimental results demonstrate that our method outperforms previous State-of-the-Art methods in various label shift scenarios, which indicates the effectiveness of the proposed method.

External IDs:doi:10.1109/taes.2026.3670773