There has been a recent emphasis on integrating physical models and deep neural networks (DNNs) for SAR target recognition, to improve performance and achieve a higher level of physical interpretability. The attributed scattering center (ASC) parameters garnered the most interest, being considered as additional input data or features for fusion in most methods. However, the performance greatly depends on the ASC optimization result, and the fusion strategy is not adaptable to different types of physical information. Meanwhile, the current evaluation scheme is inadequate to assess the model's robustness and generalizability. Thus, we propose a physics inspired hybrid attention (PIHA) mechanism and the once-for-all (OFA) evaluation protocol to address the above issues. PIHA leverages the high-level semantics of physical information to activate and guide the feature group aware of local semantics of target, so as to re-weight the feature importance based on knowledge prior. It is flexible and generally applicable to various physical models, and can be integrated into arbitrary DNNs without modifying the original architecture. The experiments involve a rigorous assessment using the proposed OFA, which entails training and validating a model on either sufficient or limited data and evaluating on multiple test sets with different data distributions. Our method outperforms other state-of-the-art approaches in 12 test scenarios with same ASC parameters. Moreover, we analyze the working mechanism of PIHA and evaluate various PIHA enabled DNNs. The experiments also show PIHA is effective for different physical information. The source code together with the adopted physical information is available at https://github.com/XAI4SAR.

翻译：近年来，将物理模型与深度神经网络（DNN）相结合用于合成孔径雷达（SAR）目标识别的研究受到广泛关注，旨在提升性能并增强物理可解释性。其中，属性散射中心（ASC）参数最受关注，多数方法将其作为额外输入数据或融合特征。然而，性能高度依赖ASC优化结果，且融合策略无法适应不同类型的物理信息。同时，现有评估方案不足以检验模型的鲁棒性和泛化能力。为此，我们提出物理启发的混合注意力（PIHA）机制与一次性（OFA）评估协议以解决上述问题。PIHA利用物理信息的高层语义激活并引导感知目标局部语义的特征组，从而基于知识先验重新加权特征重要性。该机制灵活且通用，可适配多种物理模型，并能集成至任意深度神经网络中而无需修改原始架构。实验采用所提出的OFA协议进行严格评估，包括在充足或有限数据上训练和验证模型，并在多个具有不同数据分布的测试集上评估。在采用相同ASC参数的12个测试场景中，本方法优于其他最优方法。此外，我们分析了PIHA的工作机理，并评估了多种基于PIHA的深度神经网络。实验表明，PIHA对不同物理信息均有效。源代码及所采用的物理信息已开源在https://github.com/XAI4SAR。