VSFA: Visual and Scattering Topological Feature Fusion and Alignment Network for Unsupervised Domain Adaptation in SAR Target Recognition

In recent years, how to accurately identify targets from measured synthetic aperture radar (SAR) images according to electromagnetic synthetic SAR images is attracting more and more research interest. Most existing algorithms only focus on decreasing the domain differences in visual representations, while the special characteristics of SAR images are not explored enough. Besides, these algorithms tend to align only the overall distribution of synthetic and measured images, while domain shifts between subclasses are ignored. To solve these problems, a novel unsupervised domain adaptation framework named visual and scattering topological feature fusion and alignment network (VSFA) is proposed in this article. First, considering that visual features are crucial for recognition, image reconstruction is introduced to enhance the generalization of visual features. Second, by analyzing the imaging mechanism of SAR, we explore the differences in scattering topologies between synthetic and measured images for the first time. To measure the differences quantitatively, we model the non-Euclidean scattering topology of the target as graph data and introduce graph neural networks (GNNs) to extract scattering topological features. Moreover, in order to describe the scattering topology of the target more comprehensively, we introduce two different but complementary scattering topological point extraction algorithms and achieve their fusion at the feature level by GNN for the first time. Finally, a simple but effective two-stage domain adaptation loss is proposed to constrain the network to align the distribution of synthetic and measured images class by class. Benefiting from the simultaneous reduction of distribution differences in visual space and scattering topological space, the proposed method achieves 99.15% and 98.18% recognition accuracies in two typical experiment scenarios of the Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset, demonstrating its effectiveness.