MFJA: Unsupervised Domain Adaptation Based on Multimodal Feature Fusion and Global–Local Joint Alignment for SAR ATR

In the field of synthetic aperture radar automatic target recognition (SAR ATR), inherent distributional discrepancies between electromagnetic synthetic and measured SAR images pose significant challenges to the potential applications of the former. To bridge the gap, a novel unsupervised domain adaptation framework based on Multi-modal Feature fusion and global-local Joint Alignment (MFJA) is proposed in this article. The multi-modal feature fusion focuses on describing each target more comprehensively by leveraging both visual and scattering topological information. In the visual branch, the full-aperture image is decomposed into multiple sub-aperture images to explore the scattering variations of the target at different azimuths, facilitating a richer visual description. Meanwhile, both local scattering and spatial position information of keypoints are simultaneously integrated into the feature extraction in the scattering topological branch, promoting a more comprehensive scattering topological representation. Subsequently, a gated feature fusion module is developed to effectively fuse features derived from different modalities. The global-local joint alignment aims to align different domains with greater precision. Specifically, a power normalized weighted gradient reversal layer is proposed to guide the network to focus more on hard-to-align samples during global domain alignment, thus mitigating their interference with local domain alignment. While MFJA achieves satisfactory cross-domain recognition performance, its inference efficiency is somewhat constrained. Therefore, a domain-invariant cross-modal knowledge distillation (DCKD) algorithm with a tri-path collaborative alignment strategy is further developed to distill discriminative and domain-invariant knowledge from the multi-modal model into a compact visual model based on full-aperture images, thereby accelerating inference. Experiments conducted in three scenarios on the public Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset validate the effectiveness of both MFJA and DCKD.