Stripe segmentation of oceanic internal waves in SAR images based on SegNet
The development of ocean remote sensing makes it possible to obtain valuable information from a large amount of data. Deep learning is a powerful tool that is beneficial for obtaining ocean information from remote sensing data. Oceanic internal waves play an essential role in ocean activities. To ob...
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| Published in | Geocarto international Vol. 37; no. 25; pp. 8567 - 8578 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Taylor & Francis
13.12.2022
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| Subjects | |
| Online Access | Get full text |
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| Abstract | The development of ocean remote sensing makes it possible to obtain valuable information from a large amount of data. Deep learning is a powerful tool that is beneficial for obtaining ocean information from remote sensing data. Oceanic internal waves play an essential role in ocean activities. To obtain information on irregular stripes from Synthetic Aperture Radar (SAR) images, a stripe segmentation algorithm for oceanic internal waves is proposed based on SegNet. The research results show that the proposed method can identify whether the SAR images contain oceanic internal waves and obtain the respective locations of the light and dark stripes of the oceanic internal waves from SAR images. Furthermore, because this method can accurately determine the relative locations of the light and dark stripes, it can distinguish the moment when oceanic internal waves undergo polarity conversion. |
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| AbstractList | The development of ocean remote sensing makes it possible to obtain valuable information from a large amount of data. Deep learning is a powerful tool that is beneficial for obtaining ocean information from remote sensing data. Oceanic internal waves play an essential role in ocean activities. To obtain information on irregular stripes from Synthetic Aperture Radar (SAR) images, a stripe segmentation algorithm for oceanic internal waves is proposed based on SegNet. The research results show that the proposed method can identify whether the SAR images contain oceanic internal waves and obtain the respective locations of the light and dark stripes of the oceanic internal waves from SAR images. Furthermore, because this method can accurately determine the relative locations of the light and dark stripes, it can distinguish the moment when oceanic internal waves undergo polarity conversion. |
| Author | Zhang, Hong-sheng Qi, Kai-tuo Zheng, Ying-gang Ding, Long-yu |
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| Cites_doi | 10.1080/01431161.2021.1943040 10.1109/TPAMI.2016.2644615 10.1109/36.602535 10.1080/01431160802621218 10.1007/s00343-019-9028-6 10.1080/01431161.2010.485148 10.1007/s11263-007-0090-8 10.1109/ACCESS.2020.3025946 10.1029/97JC01918 10.1134/S1028334X08040430 10.1162/neco.2006.18.7.1527 10.1016/j.rse.2004.05.014 10.1093/nsr/nwz058 10.1093/nsr/nwaa047 10.1515/jisys-2017-0033 10.1109/TPAMI.2016.2572683 10.1109/CVPR.2015.7298965 |
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| SubjectTerms | Deep learning oceanic internal waves SAR SegNet stripe segmentation |
| Title | Stripe segmentation of oceanic internal waves in SAR images based on SegNet |
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