SAR Target Depression Angle Invariant Recognition of Few-Shot Learning Via Dense Graph Prototype Network

Recently, significant advancements have been made in few-shot learning (FSL) methods based on metric learning, which have been widely applied to synthetic aperture radar (SAR) automatic target recognition. These methods typically require experimental samples to exhibit sufficiently small intraclass distances. However, in practical applications, SAR target samples are often acquired under varying depression angles, resulting in mixed images. The inherent feature deviations caused by variations in depression angles lead to increased intraclass distances in the feature distribution, which adversely affects recognition performance. To address this challenge, we propose a novel approach called the dense graph prototype network (DGP-Net). DGP-Net addresses the feature deviation problem by learning potential features and utilizes feature distribution modeling for classification. Specifically, by leveraging the information propagation mechanism of a densely connected graph convolutional network (GCN), potential features are iteratively learned while retaining previous features, thereby clustering samples of the same class with different elevation angles and eliminating feature deviations. To avoid sampling examples located at the edges of the distribution in FSL tasks, which may lead to inaccurate GCN information propagation, we introduce a “prototype node” mechanism to enhance the robustness of the model. We conduct experiments using samples from the moving and stationary target acquisition and recognition benchmark dataset with varying elevation angles for the same class. Experimental results on this dataset demonstrate the superior performance of DGP-Net. Specifically, in 3-way and 5-way classification tasks, the recognition accuracy of DGP-Net improved by over 4% compared to state-of-the-art methods.