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Published： 07 April 2023 ，
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孙宏亮，王怡然，贾童，施英妮，李晓明.2023.基于Faster R-CNN的卫星SAR图像南海海洋内波自动检测.遥感学报，27（4）： 905-918
Sun H L，Wang Y R，Jia T，Shi Y N and Li X M. 2023. Faster R-CNN based oceanic internal wave detection from SAR images in the South China Sea. National Remote Sensing Bulletin， 27（4）：905-918
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SAR（Synthetic Aperture Radar）图像上呈现出亮暗相间的条纹状特征。本文利用2001年—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。
Oceanic internal waves are widely presented in all levels of the water column in deep oceans and marginal areas. These waves 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.
The faster R-CNN is the most widely used version of the R-CNN family. This algorithm depends on the region proposal algorithms to hypothesize object locations. Based on the bright and dark stripes in the SAR images
a Faster R-CNN-based method is developed for oceanic internal wave detection. First
the oceanic internal waves are manually labeled in the 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. These images 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. The transfer learning technic is applied in the training process to transfer information from the previously learned tasks for detecting oceanic internal waves to accelerate the training process and avoid overfitting. The well-trained Faster R-CNN network can be applied by a sliding window on the SAR images to detect the oceanic internal waves. When the boundaries are obscured
the waves may be detected multiple times. In this case
the detection results will be grouped and merged. Finally
the detection results of the oceanic internal waves are acquired and recorded.
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 the SAR images with complex ocean conditions also show favorable results.
This work developed the approach for the detection of oceanic internal waves. This approach successfully transferred techniques developed in the computer vision field to solve the issues in remote sensing problems. The establishment of this method provides a technical basis for the detection of oceanic internal waves from a large amount 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.
海洋内波自动识别合成孔径雷达深度学习Faster R-CNN
oceanic internal wavesautomatic detectionSAR imagesdeep learningFaster R-CNN
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