Multiview SAR Target Recognition via View-Guided Feature Disentanglement Network

Multiview synthetic aperture radar (SAR) target recognition by mining consensus scattering features from multiple views holds significant research value. However, existing research faces two major challenges: 1) how to mitigate the degradation of private features caused by enforcing multiview feature consistency and 2) how to efficiently and stably fuse the shared features across multiple views. To address these challenges, we propose the view-guided feature disentanglement network (VFDNet) for multiview SAR target recognition. First, the shared feature alignment module (SFA) integrates both primary and associated views, employing feature alignment to bridge shared- and private-view representations. Second, the private feature adaptive global disentanglement module (PAD) incorporates the global feature-constraint module (GFCM) based on Pearson correlation, which systematically regulates feature fusion across both shared- and private-view representations. Meanwhile, to further reduce the influence of low-quality views, the feature contribution allocation module (FCAM) is used to project high-dimensional features into the reproducing kernel Hilbert space (RKHS), thereby assigning weights to each view. Extensive experiments were conducted on the FAST-Vehicle to assess the method's efficacy, with additional validation performed on benchmark datasets, including moving and stationary target acquisition and recognition (MSTAR), SRSDD, and FAIR-CSAR, to verify its robustness and generalization capability. The code of our method and dataset will be available at https://github.com/ZpSARIP/VFDNet.