Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/3773
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Saida, Shaik John | - |
dc.contributor.author | Ari, Samit | - |
dc.date.accessioned | 2022-12-01T11:02:29Z | - |
dc.date.available | 2022-12-01T11:02:29Z | - |
dc.date.issued | 2022-11 | - |
dc.identifier.citation | IEEE Silchar Subsection Conference, NIT Silchar, 4-6 November 2022 (Hybrid Mode) | en_US |
dc.identifier.uri | http://hdl.handle.net/2080/3773 | - |
dc.description | Copyright belongs to proceeding publisher | en_US |
dc.description.abstract | Ocean 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.subject | Automatic eddy detection | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Ddilated U-Net | en_US |
dc.subject | Residual path | en_US |
dc.title | Dilated Convolution based U-Net Architecture for Ocean Eddy Detection | en_US |
dc.type | Article | en_US |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
2022_IEEE_SILCON_SJSaida_Dilated.pdf | 1.55 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.