DGP-Net: Dense Graph Prototype Network for Few-Shot SAR Target Recognition
This addresses the challenge of SAR target recognition in few-shot scenarios, which is incremental as it builds on existing FSL methods with specific improvements.
The paper tackled the problem of poor recognition accuracy in few-shot learning for synthetic aperture radar (SAR) images due to feature deviation from depression angle variation, proposing DGP-Net to eliminate this deviation and achieve higher accuracy than typical methods on the MSTAR dataset.
The inevitable feature deviation of synthetic aperture radar (SAR) image due to the special imaging principle (depression angle variation) leads to poor recognition accuracy, especially in few-shot learning (FSL). To deal with this problem, we propose a dense graph prototype network (DGP-Net) to eliminate the feature deviation by learning potential features, and classify by learning feature distribution. The role of the prototype in this model is to solve the problem of large distance between congeneric samples taken due to the contingency of single sampling in FSL, and enhance the robustness of the model. Experimental results on the MSTAR dataset show that the DGP-Net has good classification results for SAR images with different depression angles and the recognition accuracy of it is higher than typical FSL methods.