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Detection of ocean internal waves based on Faster R-CNN in SAR images |
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| Authors: | Bao Sude Meng Junmin Sun Lina Liu Yongxin |
| Institution: | 1.Inner Mongolia University, Hohhot, 010021, China ;2.First Institute of Oceanography, Ministry of Natural Resources, Qingdao, 266061, China ; |
| 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 dif ferent conditions are analyzed. This paper lays a foundation for detecting ocean internal waves using convolutional neural networks. |
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| 本文献已被 CNKI SpringerLink 等数据库收录！ | |