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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Supported by the National Natural Science Foundation of China (No. 61471136), the Special Project for Global Change and Air-sea Interaction of Ministry of Natural Resources (No. GASI-02-SCS-YGST2-04), and the Chinese Association of Ocean Mineral Resources R&D (No. DY135-E2-4)
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Bao, S., Meng, J., Sun, L. et al. Detection of ocean internal waves based on Faster R-CNN in SAR images. J. Ocean. Limnol. 38, 55–63 (2020). https://doi.org/10.1007/s00343-019-9028-6
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DOI: https://doi.org/10.1007/s00343-019-9028-6