Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/3773
Title: | Dilated Convolution based U-Net Architecture for Ocean Eddy Detection |
Authors: | Saida, Shaik John Ari, Samit |
Keywords: | Automatic eddy detection Deep learning Ddilated U-Net Residual path |
Issue Date: | Nov-2022 |
Citation: | IEEE Silchar Subsection Conference, NIT Silchar, 4-6 November 2022 (Hybrid Mode) |
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. |
Description: | Copyright belongs to proceeding publisher |
URI: | http://hdl.handle.net/2080/3773 |
Appears in Collections: | Conference Papers |
Files in This Item:
File | Description | Size | Format | |
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2022_IEEE_SILCON_SJSaida_Dilated.pdf | 1.55 MB | Adobe PDF | View/Open |
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