MIGA-Net: Multi-View Image Information Learning Based on Graph Attention Network for SAR Target Recognition

Neural networks for synthetic aperture radar (SAR) automatic target recognition often encounter overfitting challenges owing to limited training samples. Moreover, the azimuth angle of SAR, a vital parameter for improving network generalization, is frequently disregarded in most models. In response, we propose MIGA-Net, a classification neural network that effectively perceives azimuthal information using multi-view images to improve classification performance. Specifically, we quantize low-dimensional azimuthal values for sample-limited scenarios. Then, we utilize encoded image sequences as training data because they encompass spatial context information compared to individual images. After extracting features of the sequence samples through convolutional layers, we design a two-layer output module. One layer converts these sequence features into graph data. Then the dense graph attention network (GAT) extracts contextual features from the graph data for angle estimation. Simultaneously, another layer combines these features for target classification. During the network training, the GAT module can extract image azimuth features with powerful information aggregation capabilities. It supervises the convolutional layers to learn azimuth features, which are fused with class features from another layer to obtain a more structured feature domain. This feature domain significantly enhances the classification performance of the network. Experiments conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset have proven the superior performance of the proposed method, achieving at least 1% higher accuracy compared to other state-of-the-art algorithms.