Learning Relation by Graph Neural Network for SAR Image Few-Shot Learning

Abstract

Supervised deep learning models usually need large amounts of labeled data due to the data-driven training strategies, and its applicability to the newly emerging categories that lack annotated images is severely limited. In contrast, few-shot learning aims to recognize novel targets from very few labeled examples, so it will be a promising method for synthetic aperture radar (SAR) image interpretation, where numerous labeled data may not exist. In this paper, we introduced a few-shot learning method based on relation network and graph neural network (GNN). Relation network extracts the feature similarity between query samples and support samples through a convolutional neural network, and it has achieved good performance in few-shot learning problems. GNNs have received increasing attention in recent years, and they have shown superior performance in relation extraction. In this work, we replaced the relation module in the relation network with attention GNN, aiming to model the relationship between the samples more effectively and learn a better metric for feature similarity. Experiments on the MSTAR dataset demonstrate that the proposed method can better extract the relationship between query samples and support samples, thereby improving the performance for few-shot image classification tasks.