基于Faster R-CNN的卫星SAR图像南海海洋内波自动检测  

Faster R-CNN based oceanic internal wave detection from SAR images in the South China Sea

作　　者：孙宏亮 王怡然 贾童 施英妮[5] 李晓明[1,3] SUN Hongliang;WANG Yiran;JIA Tong;SHI Yingni;LI Xiaoming(Hainan Key Laboratory of Earth Observation,Hainan Aerospace Information Research Institute,Chinese Academy of Sciences,Sanya 572029,China;College of Geomatics and Geoinformation,Guilin University of Technology,Guilin 541004,China;Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China;University of Chinese Academy of Sciences,Beijing 100049,China;61741 Troops,Beijing 100094,China)

机构地区：[1]中国科学院空天信息研究院海南研究院海南省地球观测重点实验室,三亚572029 [2]桂林理工大学测绘地理信息学院,桂林541004 [3]中国科学院空天信息创新研究院数字地球重点实验室,北京100094 [4]中国科学院大学,北京100049 [5]中国人民解放军61741部队,北京100094

出　　处：《遥感学报》2023年第4期905-918,共14页NATIONAL REMOTE SENSING BULLETIN

基　　金：海南省自然科学基金(编号:420RC675);国家自然科学基金(编号:41876201);国家杰出青年科学基金(编号:42025605)。

摘　　要：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 me

关 键 词：海洋内波 自动识别 合成孔径雷达 深度学习 Faster R-CNN 

分 类 号：P2[天文地球—测绘科学与技术]