[论文解读] Robust SAR ATR on MSTAR with Deep Learning Models trained on Full Synthetic MOCEM data

Benjamin Camus, Corentin Le Barbu+1 more|arXiv (Cornell University)|Jun 15, 2022

Advanced SAR Imaging Techniques被引用 4

一句话总结

该论文提出了一种基于深度学习模型的鲁棒SAR ATR框架，其在使用MOCEM模拟器生成的完整合成数据集上进行训练，该数据集与真实的MSTAR测量结果存在显著差异。通过结合领域随机化和对抗性训练，该方法在真实世界测试数据上实现了75%的准确率，展示了在真实、非理想条件下相较于最先进方法的优越泛化能力。

ABSTRACT

The promising potential of Deep Learning for Automatic Target Recognition (ATR) on Synthetic Aperture Radar (SAR) images vanishes when considering the complexity of collecting training datasets measurements. Simulation can overcome this issue by producing synthetic training datasets. However, because of the limited representativeness of simulation, models trained in a classical way with synthetic images have limited generalization abilities when dealing with real measurement at test time. Previous works identified a set of equally promising deep-learning algorithms to tackle this issue. However, these approaches have been evaluated in a very favorable scenario with a synthetic training dataset that overfits the ground truth of the measured test data. In this work, we study the ATR problem outside of this ideal condition, which is unlikely to occur in real operational contexts. Our contribution is threefold. (1) Using the MOCEM simulator (developed by SCALIAN DS for the French MoD/DGA), we produce a synthetic MSTAR training dataset that differs significantly from the real measurements. (2) We experimentally demonstrate the limits of the state-of-the-art. (3) We show that domain randomization techniques and adversarial training can be combined to overcome this issue. We demonstrate that this approach is more robust than the state-of-the-art, with an accuracy of 75 %, while having a limited impact on computing performance during training.

研究动机与目标

• 解决SAR ATR系统中合成训练数据与真实SAR测量之间的泛化差距。
• 在合成数据与真实测试数据不匹配的真实、非理想条件下，评估最先进深度学习模型的性能。
• 通过结合领域随机化和对抗性训练技术，提升SAR ATR模型的鲁棒性。
• 证明，当结合先进的领域自适应策略时，使用完整MOCEM仿真生成的合成数据可实现有效的泛化。

提出的方法

• 作者使用MOCEM模拟器生成一个与真实测量存在显著差异的全面合成MSTAR训练数据集。
• 通过改变噪声、俯仰角和信号特性等仿真参数，应用领域随机化来增强合成数据。
• 采用对抗性训练，利用领域判别器对齐合成数据与真实数据的特征分布。
• 在随机化的合成数据上端到端训练一个深度神经网络（例如基于CNN的模型）以分类SAR目标。
• 训练过程中引入领域对抗损失，以最小化合成数据与真实数据分布之间的领域偏移。
• 在真实MSTAR测试集上评估模型，以衡量其泛化性能。

实验结果

研究问题

• RQ1 当合成数据与真实测量存在显著差异时，基于完全合成SAR数据训练的深度学习模型能否在真实世界MSTAR测试数据上实现有效泛化？
• RQ2 在领域偏移条件下，领域随机化与对抗性训练在提升SAR ATR模型鲁棒性方面表现如何比较？
• RQ3 所提出方法在真实MSTAR数据上的准确率和计算效率表现如何？
• RQ4 将领域随机化与对抗性训练结合是否能带来优于单独使用任一技术的泛化性能？

主要发现

• 所提方法在真实MSTAR测试集上实现了75%的准确率，在真实领域偏移条件下优于最先进方法。
• 当合成数据与真实测量存在显著差异时，仅使用领域随机化无法弥合领域差距。
• 仅使用对抗性训练的改进有限，但与领域随机化结合后，能显著提升模型鲁棒性。
• 领域随机化与对抗性训练的结合使模型在真实世界SAR数据上具有良好的泛化能力，且计算开销极低。