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.

翻译：合成孔径雷达（SAR）图像因特殊成像机理（俯仰角变化）导致的必然特征偏差，使得识别精度低下，尤其在小样本学习（FSL）场景中更为突出。针对这一问题，我们提出密集图原型网络（DGP-Net），通过学习潜在特征消除特征偏差，并基于特征分布进行分类。该模型中原型的作用在于解决FSL中单次采样偶然性导致的同源样本间距离过大问题，并增强模型鲁棒性。在MSTAR数据集上的实验结果表明，DGP-Net对不同俯仰角SAR图像具有良好分类效果，其识别精度高于典型FSL方法。