数字海洋与水下攻防2024，Vol.7Issue(5)：521-528,8.DOI:10.19838/j.issn.2096-5753.2024.05.008
基于深度学习的星载SAR海洋内波自动检测研究
Automatic Detection of Internal Waves from Space-borne SAR Images Based on Deep Learning
摘要
Abstract
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.关键词
海洋内波/合成孔径雷达/深度学习/自动检测Key words
internal wave/SAR/deep learning/automatic detection分类
天文与地球科学引用本文复制引用
范开国,王业桂,姜翰,徐东洋,胡旭辉,施英妮..基于深度学习的星载SAR海洋内波自动检测研究[J].数字海洋与水下攻防,2024,7(5):521-528,8.基金项目
上海市产业协同创新(科技)项目"区域海洋环境监测预警大数据云平台"(XTCX-KJ-2022-20). （科技）
