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Cite this paper: |
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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 |
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Detection of ocean internal waves based on Faster R-CNN in SAR images |
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| BAO Sude1, MENG Junmin2, SUN Lina2, LIU Yongxin1 |
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1 Inner Mongolia University, Hohhot 010021, China;
2 First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China |
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| Abstract: |
| 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. |
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| 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)
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| Received: 2019-02-19 Revised: |
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