Dual-Stream Manifold Multiscale Network for Target Recognition in Complex-Valued SAR Image With Electromagnetic Feature Fusion

Existing deep learning-based methods for synthetic aperture radar (SAR) target recognition typically rely solely on the amplitude images without considering the complex characteristic of SAR images, making it difficult to recognize SAR targets with high visual similarity. To solve this issue, a novel dual-stream manifold multiscale network fused with electromagnetic features, i.e., EFMM-Net is proposed for target recognition in complex-valued SAR images. In EFMM-Net, the attributed scattering center (ASC) model is first utilized to reconstruct the complex-valued SAR image, thereby highlighting the electromagnetic scattering features of the target. Subsequently, the reconstructed complex-valued SAR image is combined with the original one to construct the dual-stream input. Second, a scattering-guided manifold multiscale (SGMM) backbone is proposed for parallel extraction of data features and electromagnetic scattering features of the target from the dual-stream input. During feature extraction, the SGMM backbone can effectively leverage the phase information of complex-valued SAR images and inject target scattering information into data features through scattering-guided channel-wise feature alignment, thus enhancing the awareness of data features to critical scattering characteristics. Finally, to effective fuse the data features and electromagnetic scattering features, a location awareness feature fusion (LAFF) recognition module is proposed. By exploiting coordinate attention, LAFF utilizes the target location information captured from electromagnetic scattering features to direct the feature fusion process, thereby increasing the focus of fusion features on the target region. The extensive recognition results of three-class and six-class ship targets in the OpenSARShip 2.0 dataset demonstrate the effectiveness and superiority of the proposed method.