海洋内波广泛存在于世界各大洋和边缘海中，在海洋能量串级中扮演着重要角色，在海洋资源开发、海洋工程建设和海洋军事活动等方面均具有重要学术价值与实际意义。海洋内波在合成孔径雷达（Synthetic Aperture Radar，SAR）图像上呈现出亮暗相间的条纹状特征。本文利用2001-2020年南海海域包含不同微波波段（C、L、X）、不同极化方式、不同空间分辨率的631幅星载SAR图像，构建了5480个SAR图像南海海洋内波样本，结合Faster R-CNN网络框架，利用迁移学习的方法，实现SAR图像上的海洋内波自动检测。模型识别准确率达到95.7%，召回率为92.3%，在准确率较高的同时还能保持较低的虚警率。该算法的建立使得基于海量卫星SAR数据检出海洋内波成为可能，从而为针对性地开展内波动力参数反演和过程研究提供了技术和数据基础。

Objective Oceanic internal waves are widely presented in all levels of the water column in deep oceans as well as in marginal areas. They play an important role in seawater energy exchange. The study of oceanic internal waves has important academic values and practical significance in marine resources, marine engineering, and the marine military. The oceanic internal waves are distinct bright and dark stripes in Synthetic Aperture Radar (SAR) images. Those stripes can serve as clues to efficiently identify the oceanic internal waves from SAR images. The growing popularity of computer vision has led to the wide adoption of deep learning for the detection of oceanic features in remote sensing data. In this study, we intend to apply the Faster R-CNN, a state-of-the-art deep learning method, to the automatic detection of oceanic internal waves. Method The Faster R-CNN is the most widely used version of the R-CNN family. It depends on region proposal algorithms to hypothesize object locations. Based on bright and dark stripes in SAR images, a Faster R-CNN-based method is developed for oceanic internal wave detection. First, the oceanic internal waves are manually labeled in SAR images to serve as the training set. The training set for the detection method contains 5480 SAR images, which are in multi-band, multi-polarization mode, and multi-spatial scale. They are collected in the South China Sea region from 2001 to 2020. Then, the Faster R-CNN network is trained based on the obtained training set. Meanwhile, the parameters (such as training epochs) are optimized. To accelerate the training process and avoid overfitting, the transfer learning technic is also applied in the training process to transfer information from previously learned tasks for detecting oceanic internal waves. The well-trained Faster R-CNN network can be applied by sliding window on SAR images to detect the oceanic internal waves. With obscure boundaries, the waves may be detected multiple times. In this case, the detection results will be grouped and merged. Finally, the detection results of oceanic internal waves are acquired and recorded. Result The evaluations are conducted on multi-source SAR data, showing that the accuracy rate (AP) and recall rate (AR) of the developed method are up to 95.7% and 92.3%, respectively. This method can achieve high accuracy while keeping the false alarm rate relatively low. The experiments on SAR images with complex ocean conditions also show favorable results. Conclusion In this research, approaches for the detection of oceanic internal waves were developed. This approach successfully transferred techniques developed in the computer vision field to solve the problem in remote sensing problems. The establishment of this method provides a technical basis for the detection of oceanic internal waves from huge amounts of SAR data and further promotes the research of internal wave parameter inversion and dynamic processes. In addition, the proposed method was initially designed for lunar research, but it could be applied to the detection of other oceanic features.