Synthetic Aperture Radar (SAR) images are commonly utilized in military applications for automatic target recognition (ATR). Machine learning (ML) methods, such as Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN), are frequently used to identify ground-based objects, including battle tanks, personnel carriers, and missile launchers. Determining the vehicle class, such as the BRDM2 tank, BMP2 tank, BTR60 tank, and BTR70 tank, is crucial, as it can help determine whether the target object is an ally or an enemy. While the ML algorithm provides feedback on the recognized target, the final decision is left to the commanding officers. Therefore, providing detailed information alongside the identified target can significantly impact their actions. This detailed information includes the SAR image features that contributed to the classification, the classification confidence, and the probability of the identified object being classified as a different object type or class. We propose a GNN-based ATR framework that provides the final classified class and outputs the detailed information mentioned above. This is the first study to provide a detailed analysis of the classification class, making final decisions more straightforward. Moreover, our GNN framework achieves an overall accuracy of 99.2\% when evaluated on the MSTAR dataset, improving over previous state-of-the-art GNN methods.

翻译：合成孔径雷达（SAR）图像在军事目标自动识别（ATR）中具有广泛应用。卷积神经网络（CNN）和图神经网络（GNN）等机器学习（ML）方法常被用于识别地面目标，包括主战坦克、装甲运兵车和导弹发射装置。确定车辆类别（如BRDM2坦克、BMP2坦克、BTR60坦克和BTR70坦克）至关重要，这有助于判别目标对象是友军还是敌军。尽管机器学习算法能提供识别目标的反馈，但最终决策权仍由指挥官掌握。因此，在识别目标的同时提供详细信息将对指挥官的行动产生重要影响。这些详细信息包括：用于分类的SAR图像特征、分类置信度，以及识别对象被误判为其他类型或类别的概率。本文提出基于GNN的ATR框架，该框架不仅能输出最终分类类别，还能提供上述详细信息。本研究首次提供了分类类别的详细分析，使最终决策更加直观。在MSTAR数据集上评估时，我们的GNN框架总体准确率达到99.2%，优于先前最先进的GNN方法。