Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3773
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dc.contributor.authorSaida, Shaik John-
dc.contributor.authorAri, Samit-
dc.date.accessioned2022-12-01T11:02:29Z-
dc.date.available2022-12-01T11:02:29Z-
dc.date.issued2022-11-
dc.identifier.citationIEEE Silchar Subsection Conference, NIT Silchar, 4-6 November 2022 (Hybrid Mode)en_US
dc.identifier.urihttp://hdl.handle.net/2080/3773-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractOcean eddies have a significant effect on the maritime environment. They are necessary for carrying a variety of ocean traces across the ocean. Eddy detection is one of the most active fields of physical oceanographic research. Although it is a new trend, using deep learning algorithms to find eddies is still in its early stages. The different sizes and shapes of eddies make automatic eddy segmentation challenging. U-Net makes a dense prediction to solve this problem. However, the network architecture is very intricate. In this paper, a dilated convolution U-Net is developed for the semantic segmentation of ocean eddies using sea surface height data. This technique decreases architectural complexity without sacrificing performance. Further, a new residual path is proposed to cascade encoder outputs with the decoder. The experimental results demonstrate that the proposed architecture outperforms the existing deep learning techniques for eddy detection.en_US
dc.subjectAutomatic eddy detectionen_US
dc.subjectDeep learningen_US
dc.subjectDdilated U-Neten_US
dc.subjectResidual pathen_US
dc.titleDilated Convolution based U-Net Architecture for Ocean Eddy Detectionen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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