Images generated by high-resolution SAR have vast areas of application as they can work better in adverse light and weather conditions. One such area of application is in the military systems. This study is an attempt to explore the suitability of current state-of-the-art models introduced in the domain of computer vision for SAR target classification (MSTAR). Since the application of any solution produced for military systems would be strategic and real-time, accuracy is often not the only criterion to measure its performance. Other important parameters like prediction time and input resiliency are equally important. The paper deals with these issues in the context of SAR images. Experimental results show that deep learning models can be suitably applied in the domain of SAR image classification with the desired performance levels.

翻译：高分辨率SAR生成的图像具有广泛的应用领域，因为它们可以在不利的光线和天气条件下表现更好。军事系统是其中的一个应用领域。本研究试图探索当前计算机视觉领域引入的最先进模型对SAR目标分类（MSTAR）的适用性。由于针对军事系统的任何解决方案的应用都将是战略性和实时性的，因此精度通常不是衡量其性能的唯一标准。其他重要参数，如预测时间和输入弹性同等重要。本文在SAR图像的背景下处理了这些问题。实验结果表明，深度学习模型可以在SAR图像分类领域得到恰当的应用并达到所需的性能水平。