SAR image classification naturally has to deal with huge noise and a high dynamic range particularly requiring robust classification models. Additionally, the deployment of these models on edge devices, such as drones and military aircraft, requires a careful balance between model size and classification accuracy. This study explores the potential of tensor networks to meet these robustness requirements, specifically evaluating their resilience to data poisoning. Unlike previous works that concentrated on conventional neural networks for SAR object detection, this research focuses on the robustness and model reduction capabilities of tensor networks in object classification. Our findings indicate that tensor networks are adept at addressing both the challenges of robustness and the need for model efficiency, thereby contributing valuable insights to the ongoing discourse in radar applications and deep learning methodologies in general.

翻译：SAR图像分类天然需处理大量噪声及高动态范围，尤其依赖鲁棒的分类模型。此外，将这些模型部署于无人机、军用飞机等边缘设备时，需在模型规模与分类精度间谨慎权衡。本研究探索张量网络满足这些鲁棒性要求的潜力，重点评估其对数据投毒攻击的抵御能力。不同于以往聚焦于传统神经网络进行SAR目标检测的研究，本工作着眼于张量网络在目标分类中的鲁棒性与模型缩减能力。研究结果表明，张量网络既能有效应对鲁棒性挑战，又可满足模型效率需求，从而为雷达应用与深度学习方法的持续探讨提供重要见解。