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

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