Vehicle recognition is a fundamental problem in SAR image interpretation. However, robustly recognizing vehicle targets is a challenging task in SAR due to the large intraclass variations and small interclass variations. Additionally, the lack of large datasets further complicates the task. Inspired by the analysis of target signature variations and deep learning explainability, this paper proposes a novel domain alignment framework named the Hierarchical Disentanglement-Alignment Network (HDANet) to achieve robustness under various operating conditions. Concisely, HDANet integrates feature disentanglement and alignment into a unified framework with three modules: domain data generation, multitask-assisted mask disentanglement, and domain alignment of target features. The first module generates diverse data for alignment, and three simple but effective data augmentation methods are designed to simulate target signature variations. The second module disentangles the target features from background clutter using the multitask-assisted mask to prevent clutter from interfering with subsequent alignment. The third module employs a contrastive loss for domain alignment to extract robust target features from generated diverse data and disentangled features. Lastly, the proposed method demonstrates impressive robustness across nine operating conditions in the MSTAR dataset, and extensive qualitative and quantitative analyses validate the effectiveness of our framework.

翻译：车辆识别是SAR图像解译中的基础问题。然而，由于类内差异大、类间差异小，在SAR中稳健识别车辆目标是一项具有挑战性的任务。此外，大型数据集的匮乏进一步加剧了该任务的复杂性。受目标特征变化分析与深度学习可解释性的启发，本文提出了一种新颖的域对齐框架——层级式解缠-对齐网络（HDANet），旨在实现多种运行条件下的稳健性。具体而言，HDANet将特征解缠与对齐整合为统一框架，包含三个模块：域数据生成、多任务辅助掩膜解缠以及目标特征的域对齐。第一模块生成用于对齐的多样化数据，并设计了三种简单有效的数据增强方法来模拟目标特征变化。第二模块利用多任务辅助掩膜将目标特征与背景杂波解缠，以防止杂波干扰后续对齐。第三模块采用对比损失进行域对齐，从生成的多样化数据与解缠特征中提取稳健的目标特征。最后，所提方法在MSTAR数据集的九种运行条件下展现出显著的稳健性，大量定性与定量分析验证了该框架的有效性。