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  

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

作　　者：何奇山 赵凌君 计科峰[1] 匡纲要[1] HE Qishan;ZHAO Lingjun;JI Kefeng;KUANG Gangyao(Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System,College of Electronic Science and Technology,National University of Defense Technology,Changsha 410073,China)

机构地区：[1]国防科技大学电子科学学院电子信息系统复杂电磁环境效应实验室,长沙410073

出　　处：《电子与信息学报》2024年第10期3827-3848,共22页Journal of Electronics & Information Technology

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。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.

关 键 词：合成孔径雷达 自动目标识别 深度学习 域自适应 参数敏感性 

分 类 号：TN958[电子电信—信号与信息处理]