  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.
