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BAO Sude, MENG Junmin, SUN Lina, LIU Yongxin. Detection of ocean internal waves based on Faster R-CNN in SAR images[J]. Journal of Oceanology and Limnology, 2020, 38(1): 55-63

Detection of ocean internal waves based on Faster R-CNN in SAR images

BAO Sude1, MENG Junmin2, SUN Lina2, LIU Yongxin1
1 Inner Mongolia University, Hohhot 010021, China;
2 First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
Ocean internal waves appear as irregular bright and dark stripes on synthetic aperture radar (SAR) remote sensing images. Ocean internal waves detection in SAR images consequently constituted a difficult and popular research topic. In this paper, ocean internal waves are detected in SAR images by employing the faster regions with convolutional neural network features (Faster R-CNN) framework; for this purpose, 888 internal wave samples are utilized to train the convolutional network and identify internal waves. The experimental results demonstrate a 94.78% recognition rate for internal waves, and the average detection speed is 0.22 s/image. In addition, the detection results of internal wave samples under different conditions are analyzed. This paper lays a foundation for detecting ocean internal waves using convolutional neural networks.
Key words:    ocean internal waves|faster regions with convolutional neural network features (Faster R-CNN)|convolutional neural network|synthetic aperture radar (SAR) image|region proposal network (RPN)   
Received: 2019-02-19   Revised:
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Articles by BAO Sude
Articles by MENG Junmin
Articles by SUN Lina
Articles by LIU Yongxin
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