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：高分辨率合成孔径雷达（SAR）生成的图像在恶劣光照和天气条件下仍能有效工作，因而具有广泛的应用领域，其中军事系统是典型应用场景之一。本研究旨在探索当前计算机视觉领域最先进模型在SAR目标分类（MSTAR）中的适用性。由于军事系统解决方案的应用具有战略性和实时性要求，准确率并非衡量其性能的唯一标准，预测时间与输入鲁棒性等关键参数同样重要。本文针对SAR图像场景下的这些问题展开研究。实验结果表明，深度学习模型可在SAR图像分类领域实现预期的性能水平并得到有效应用。