  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.
