近年来,合成孔径雷达成像技术因具备全天时和全天候的目标感测能力,在海洋实时监测和管控等领域发挥着重要作用,特别是高分率SAR图像中的舰船目标检测成为当前的研究热点之一。首先分析基于深度学习的SAR图像舰船目标检测流程,并对样本训练数据集的构建、目标特征的提取和目标框选的设计等关键步骤进行归纳总结。然后对检测流程中的各部分对SAR图像舰船目标检测精度和速度的影响进行对比分析。最后根据当前研究现状,深入分析深度学习算法在舰船检测应用中存在的问题,探讨基于深度学习的SAR图像舰船目标检测的进一步研究方向。

In recent years, synthetic aperture radar imaging technology (SAR) has played an important role in the real-time monitoring and control of the ocean due to its all-time and all-weather target sensing capabilities. In particular, the detection of ship targets in high-resolution SAR images has become current one of the research hotspots. First, the process of ship target detection based on deep learning in SAR images is analyzed, and the key steps such as the construction of sample training datasets are summarized, the extraction of target features and the design of target frame selection. Then, the influence of each part of the detection process on the detection accuracy and speed of the ship target in the SAR image is compared and analyzed. Finally, according to the current research status, the problems of deep learning algorithms in the application of ship detection are deeply analyzed, and the further research direction of ship target detection based on deep learning in SAR images is discussed.