Research Progress of Deep Learning Technology for Imaging Parameter Sensitivity of SAR Target Recognition
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摘要: 随着人工智能技术的发展，基于深度神经网络的合成孔径雷达(SAR)目标识别得到了广泛关注。然而，SAR系统的成像机制导致了图像特性与成像参数之间的强相关性，因此深度学习框架下的目标识别算法精度极易受成像参数敏感性的干扰，这成为了制约先进智能算法部署到实际工程中的一大障碍。该文首先回顾了SAR图像目标识别技术的发展与相关数据集，从雷达工作的成像几何、载荷参数和噪声干扰3个角度，深入分析了成像参数变化对图像特性的影响；然后，从模型、数据、特征3个维度，总结归纳了现有文献关于深度学习技术对成像参数敏感性的鲁棒性与泛化性这一问题的研究进展；接下来，汇总并分析了典型方法的实验结果；最后讨论了在未来有望突破成像参数敏感性这一问题的深度学习技术研究方向。Abstract: With the development of artificial intelligence technology, Synthetic Aperture Radar (SAR) target recognition based on deep neural networks has received widespread attention. However, the imaging mechanism of SAR system leads to a strong correlation between image characteristics and imaging parameters, so the algorithm accuracy under deep learning is easily disturbed by the sensitivity of imaging parameters, which becomes a major obstacle restricting the deployment of advanced intelligent algorithms to practical engineering applications. Firstly, in this paper, the developments of SAR image target recognition technology and related data sets are reviewed, and the influence of imaging parameters on image characteristics is analyzed deeply from three aspects, i.e., imaging geometry, radar parameter and noise interference. Then, the existing literature on the robustness and generalization of deep learning technology to imaging parameter sensitivity is summarized from the three dimensions of model, data and features. Thereafter, the experimental results of typical methods are summarized and analyzed. Finally, the research direction of deep learning technology which is expected to break through the sensitivity of imaging parameters in the future is discussed.
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表 1 SAR目标识别开源数据集
来源 数据集 目标类型 采集\仿真平台 主要成像参数特点 实测 MSTAR[7] 军用车辆 机载SAR (1) X波段，HH极化，带宽561 MHz，分辨率0.3 m
(2) 覆盖15°,17°,30°和45° 4个俯仰角，0°～360°方位角(部分严重散焦图像被剔除)Gotcha[18,19] 民用车辆 机载SAR (1) X波段，全极化，带宽640 MHz
(2) 均匀覆盖43.7°～45°中8个俯仰角，0°～360°方位角CircularSAR[20] 军用车辆 机载SAR (1) X波段，带宽 1800 MHz，分辨率0.1 m
(2) 覆盖15°,26°,31°和45° 4个俯仰角，0°～360°方位角(部分严重散焦图像被剔除)SAR-ACD[21] 民用飞机 GF3 C波段，HH极化，分辨率1 m OpenSARShip-1.0/2.0[22,23] 民用舰船 Sential-1 C波段，VV和VH极化，分辨率20～22 m FuSAR-Ship[24] 民用舰船 GF3 C波段，HH和VV极化，分辨率1.5 m 仿真 SarSIM[25] 民用车辆 CST软件 (1) X波段，HH极化，分辨率0.3 m, 3种地面环境
(2) 覆盖15°,17°,25°,30°,35°,40°和45° 7个俯仰角，0°～360°方位角(5°为间隔)SAMPLE[26] 军用车辆 XPatch软件 (1) X波段，HH极化，带宽561 MHz，分辨率0.3 m
(2) 覆盖15°～17°俯仰角，10°～80°方位角
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表 2 优缺点及代表性方法特点总结
技术类型 优缺点 代表性参考文献 主要特点 模型端 (1) 提升融合后特征的物理可解释性
(2) 传统/物理特征的鲁棒性仍有提升空间文献[27] 将CNN模型与电磁散射特征融合 文献[28] 将CNN模型与传统几何特征融合 数据端 (1) 仅需在数据端操作，易于工程实现
(2) 性能受到扩增部分数据的质量影响文献[29] 使用仿射变化、图像旋转扩增训练集 文献[30] 使用生成对抗网络扩增训练集 文献[31] 使用电磁仿真数据扩增训练集 特征端 (1) 泛化性提升显著，存在理论基础
(2) 直推式学习限制实际应用场景文献[32] 在特征层上对齐分布 文献[33] 在特征层+像素层上对齐分布 文献[34] 在特征层+像素层+决策层上对齐分布
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表 4 机载SAR车辆目标数据集的成像参数
参数 FARAD Ka FARAD X miniSAR 成像地点 美国科特兰空军基地 美国新墨西哥州 美国新墨西哥州 成像时间 2015.08 2015.10 2005.05 波段 Ka X Ku 中心频率(GHz) 35.6 9.6 16.8 带宽(GHz) 5 3 3 俯仰角度(°) 26～34 26～34 26～29 分辨率(m) 0.1 0.1 0.1 最大观测距离(km) 6 12 8
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表 5 舰船检测数据集中的四种星载SAR成像参数
参数 Gaofen-3 TerraSAR-X Radarsat-2 Sentinel-1 轨道高度(km) 755 514 798 693 入射角度(°) 10～60 20～55 20～45 10～60 波段 C X C C 带宽(MHz) 240 150 100 100 分辨率(m) 0.5～100 1～16 1～100 5～20 成像范围(km) 10～650 5～100 20～50 20～400 俯仰扫描角度(°) ±20 ±25 ±11 ±20
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表 6 SOC与EOC条件中俯仰角变化情况
俯仰角(°) 类别数量 训练数据 测试数据 SOC(17°～15°) 17 15 10 EOC(17°～30°) 17 30 3 EOC(17°～45°) 17 45 3
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