用两种不同模拟器合成数据，提升雷达图像识别模型泛化能力。
Combining SAR Simulators to Train ATR Models with Synthetic Data
- 融合两种雷达仿真模型生成合成数据
- 在MSTAR数据集上达到88%识别准确率
- 适合需要高泛化能力的遥感目标识别研究者
本文旨在训练深度学习模型以实现合成孔径雷达（SAR）图像中的自动目标识别（ATR）。为解决真实标注数据稀缺问题，采用由SAR模拟器生成的合成数据。模拟可完全控制虚拟环境，便于生成大规模多样化数据集，但其基于简化物理模型，导致合成数据与真实测量存在偏差，使仅在合成数据上训练的ATR模型难以泛化到真实场景。本文贡献有二：一是量化分析仿真范式对ATR性能的影响；二是提出新方法——结合两种基于不同原理的模拟器（MOCEM，基于散射中心模型；Salsa，基于光线追踪）生成互补的合成数据。使用MOCEM与Salsa联合生成的数据集，配合提出的ADASCA深度学习方法，在MSTAR实测数据上取得近88%的识别准确率。
原文摘要 · Abstract (English)
This work aims to train Deep Learning models to perform Automatic Target Recognition (ATR) on Synthetic Aperture Radar (SAR) images. To circumvent the lack of real labelled measurements, we resort to synthetic data produced by SAR simulators. Simulation offers full control over the virtual environment, which enables us to generate large and diversified datasets at will. However, simulations are intrinsically grounded on simplifying assumptions of the real world (i.e. physical models). Thus, synthetic datasets are not as representative as real measurements. Consequently, ATR models trained on synthetic images cannot generalize well on real measurements. Our contributions to this problem are twofold: on one hand, we demonstrate and quantify the impact of the simulation paradigm on the ATR. On the other hand, we propose a new approach to tackle the ATR problem: combine two SAR simulators that are grounded on different (but complementary) paradigms to produce synthetic datasets. To this end, we use two simulators: MOCEM, which is based on a scattering centers model approach, and Salsa, which resorts on a ray tracing strategy. We train ATR models using synthetic dataset generated both by MOCEM and Salsa and our Deep Learning approach called ADASCA. We reach an accuracy of almost 88 % on the MSTAR measurements.
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