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面向SAR目标识别成像参数敏感性的深度学习技术研究进展

何奇山 赵凌君 计科峰 匡纲要

何奇山, 赵凌君, 计科峰, 匡纲要. 面向SAR目标识别成像参数敏感性的深度学习技术研究进展[J]. 电子与信息学报, 2024, 46(10): 3827-3848. doi: 10.11999/JEIT240155
引用本文: 何奇山, 赵凌君, 计科峰, 匡纲要. 面向SAR目标识别成像参数敏感性的深度学习技术研究进展[J]. 电子与信息学报, 2024, 46(10): 3827-3848. doi: 10.11999/JEIT240155
HE Qishan, ZHAO Lingjun, JI Kefeng, KUANG Gangyao. Research Progress of Deep Learning Technology for Imaging Parameter Sensitivity of SAR Target Recognition[J]. Journal of Electronics & Information Technology, 2024, 46(10): 3827-3848. doi: 10.11999/JEIT240155
Citation: HE Qishan, ZHAO Lingjun, JI Kefeng, KUANG Gangyao. Research Progress of Deep Learning Technology for Imaging Parameter Sensitivity of SAR Target Recognition[J]. Journal of Electronics & Information Technology, 2024, 46(10): 3827-3848. doi: 10.11999/JEIT240155

面向SAR目标识别成像参数敏感性的深度学习技术研究进展

doi: 10.11999/JEIT240155
详细信息
    作者简介:

    何奇山:男,博士生,研究方向为SAR图像目标检测与识别

    赵凌君:女,副教授,研究方向为遥感信息处理,SAR目标自动识别

    计科峰:男,教授,研究方向为合成孔径SAR目标电磁散射特性建模、特征提取、检测识别以及多源空天遥感图像智能处理与解译基础理论、核心关键技术以及系统集成与应用

    匡纲要:男,教授,研究方向为微波成像技术、遥感图像智能解译、目标电测建模与散射特性分析、SAR图像目标检测与识别

    通讯作者:

    赵凌君 nudtzlj@163.com

  • 中图分类号: TN958

Research Progress of Deep Learning Technology for Imaging Parameter Sensitivity of SAR Target Recognition

  • 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。
  • 图  1  不同成像条件下的SAR图像

    图  2  SAR成像倾斜投影几何

    图  3  本文对现有研究文献的简要概括

    图  4  各类型属性散射中心示意图

    图  5  不同分辨率条件下SAR图像重构方法

    图  6  域偏移与域自适应示意图

    图  7  可见光和SAR图像(车辆目标)

    图  8  可见光和SAR图像(舰船目标)

    图  9  可微分SAR图像渲染器

    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
    下载: 导出CSV

    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
    下载: 导出CSV

    表  3  不同成像条件变化及其数据增强策略

    成像条件变化种类 数据增强策略 参考文献
    俯仰角变化 仿射变化,距离向重采样 文献[29,49,67]
    方位角变化 角度插值,生成对抗,电磁仿真 文献[2931,6871]
    分辨率变化 频域2维子带分解 文献[27,72,73]
    噪声环境干扰 噪声对抗样本,部分散射重构 文献[68,7380]
    下载: 导出CSV

    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
    下载: 导出CSV

    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
    下载: 导出CSV

    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°)171510
    EOC(17°~30°)17303
    EOC(17°~45°)17453
    下载: 导出CSV

    表  7  MSTAR数据集上典型方法总体识别率(OA)对比(%)

    方法 类型 SOC(17°~15°) EOC(17°~30°) EOC(17°~45°)
    A-ConvNet[11] 模型端 99.13 97.42 64.17
    文献[80] 数据端 99.48 98.61 74.48
    FEC[27] 模型端 99.52 99.19 81.08
    ASC-MACN[64] 模型端 99.63 99.42
    TDDA[32] 特征端 99.11 99.17 86.65
    SDF-Net[46] 模型端 99.58 99.20 86.57
    下载: 导出CSV

    表  8  Gaofen3和SSDD上典型方法异源检测性能对比(%)

    方法 Gaofen3®SSDD SSDD®Gaofen3
    PR RE mAP PR RE mAP
    FasterRCNN[119] 62.5 77.8 67.0 57.7 71.0 57.9
    文献[111] 74.6 82.9 78.1 69.8 79.9 68.4
    文献[110] 78.4 86.3 81.5 73.7 81.9 74.4
    文献[112] 79.8 86.3 83.6 74.8 83.3 77.0
    下载: 导出CSV
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  • 收稿日期:  2024-03-08
  • 修回日期:  2024-07-21
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