Position-Aware Graph Neural Network for Few-shot SAR Target Classification

Synthetic aperture radar (SAR) target classification methods based on convolutional neural networks (CNN) are susceptible to overfitting due to limited samples. Additionally, the position and deformation variations of SAR targets can also affect the feature extraction capabilities of CNN. To address these issues, this paper proposes a novel few-shot learning (FSL) method, called position-aware graph neural network (PA-GNN). The PA-GNN introduces a PAM to enhance the feature representation of the target. Specifically, deformable convolution is incorporated into the embedding network to accommodate the various shapes of SAR targets. Then, the self-attention network is brought in to capture the spatial dependence of any two positions in the feature maps, and the cross-attention network calculates the cross-attention between support and query feature maps to handle the problem of unseen categories. These two attention networks can be exploited to highlight the target regions. Besides, to integrate the features extracted by these two attention networks, an adaptive feature fusion module is designed to obtain discriminative features of the target position. The PA-GNN conducts measurement by constructing multiple layers of GNN and utilizes residual learning to alleviate over-smoothing. Furthermore, we introduce a fused loss to enhance the separability between different categories and the similarity within the same category. Experiments on OpenSARShip and MSTAR datasets demonstrate that the proposed PA-GNN can achieve comparable classification results compared with other FSL algorithms.