Automatic Detection of Internal Waves from Space-borne SAR Images Based on Deep Learning
Internal waves are a kind of seawater fluctuation caused by steep change of seawater density and external disturbance,which are usually shown as bright and dark stripes on Synthetic Aperture Radar(SAR)remote sensing images.In this paper,a training and validation dataset is constructed based on 390 Sentinel-1 SAR internal wave remote sensing images from 2014 to 2021.Combined with the algorithm of Rotation Equivariant Detector(ReDet),the transfer learning method is used to train the model,and an automatic detection model for internal waves is obtained based on the rotating box.The detection results are compared with those from YOLOv8 model.The results show that the rotating target detection model performs better than YOLOv8 in automatic detection of internal waves,which yields an accuracy rate of 93.06%with a recall rate of 90.24%and achieves a high accuracy and a low false alarm at the same time.The rotating target detection model provides an innovative technical solution for automatic and rapid detection of internal waves among massive space-borne SAR images.The method can be used to extract the propagation direction information of internal waves,which provides a solid technical basis for dynamic parameter inversion and further process research of internal waves.
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