  Mainstream DNN-based SAR-ATR methods still face issues such as easy
overfitting of a few training data, high computational overhead, and poor
interpretability of the black-box model. Integrating physical knowledge into
DNNs to improve performance and achieve a higher level of physical
interpretability becomes the key to solving the above problems. This paper
begins by focusing on the electromagnetic (EM) backscattering mechanism. We
extract the EM scattering (EMS) information from the complex SAR data and
integrate the physical properties of the target into the network through a
dual-stream framework to guide the network to learn physically meaningful and
discriminative features. Specifically, one stream is the local EMS feature
(LEMSF) extraction net. It is a heterogeneous graph neural network (GNN) guided
by a multi-level multi-head attention mechanism. LEMSF uses the EMS information
to obtain topological structure features and high-level physical semantic
features. The other stream is a CNN-based global visual features (GVF)
extraction net that captures the visual features of SAR pictures from the image
domain. After obtaining the two-stream features, a feature fusion subnetwork is
proposed to adaptively learn the fusion strategy. Thus, the two-stream features
can maximize the performance. Furthermore, the loss function is designed based
on the graph distance measure to promote intra-class aggregation. We discard
overly complex design ideas and effectively control the model size while
maintaining algorithm performance. Finally, to better validate the performance
and generalizability of the algorithms, two more rigorous evaluation protocols,
namely once-for-all (OFA) and less-for-more (LFM), are used to verify the
superiority of the proposed algorithm on the MSTAR.
