  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.
