Research on SAR Image Target Recognition Method Based on Multi-Dimensional Feature Fusion

Synthetic Aperture Radar (SAR) has broad application prospects in target recognition; however, intrinsic multiplicative speckle noise, geometric distortions, and the complex coupling of multimodal features often limit the comprehensiveness of representations and the efficiency of fusion, thereby restricting recognition accuracy. To address these limitations, this paper proposes a SAR image target recognition method based on multidimensional feature fusion. The proposed method first achieves noise suppression and contrast enhancement through an optimized preprocessing layer. Subsequently, a dual-branch hierarchical feature extraction network synchronously captures low-dimensional physical prior features driven by domain knowledge and highly discriminative deep convolutional features, ensuring a balance between physical interpretability and high-capacity representation. Finally, a variance-adaptive weighted fusion layer dynamically balances the contribution of different feature streams, mitigating information redundancy and feature conflict. Quantitative experiments on the MSTAR and public CETC38-SAR datasets demonstrate that under various pre-trained backbones, the proposed framework improves precision, recall, and F1-score by 5%–15% compared with baseline methods. Ablation studies and evaluations under extended operating conditions further validate the robustness, computational efficiency, and structural validity of the decoupled architecture.