Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3684
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dc.contributor.authorSaida, Shaik John-
dc.contributor.authorAri, Samit-
dc.date.accessioned2022-06-06T07:23:20Z-
dc.date.available2022-06-06T07:23:20Z-
dc.date.issued2022-05-
dc.identifier.citationTwenty-Eighth National Conference on Communications (NCC-2022), IIT Bombay, 23-27 May 2022en_US
dc.identifier.urihttp://hdl.handle.net/2080/3684-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractOcean eddies are a common occurrence in ocean water circulation. They have an enormous impact on the marine ecosystem. One of the most active study topics in physical oceanography is ocean eddy detection. Although using deep learning algorithms to detect eddies is a recent trend, it is still in its infancy. In this paper, an attention mechanism-based ocean eddy detection approach using deep learning is proposed. Attention mechanism has spatial and channel attention modules that are cascaded to convolution blocks-based encoder model to simulate spatial and channel semantic interdependencies. In the spatial attention module, the feature at each point is aggregated selectively by the sum of the features at all positions. The channel attention module aggregates related data from all channel maps to selectively highlight interdependent channel maps. The original feature map and the feature map obtained through the attention mechanism are appended to enhance the feature representation further, resulting in more accurate segmentation results. The findings of the experiments show that adopting an attentionbased deep framework improves eddy recognition accuracy significantlyen_US
dc.subjectDeep learningen_US
dc.subjectSemantic segmentationen_US
dc.subjectAttention mechanismen_US
dc.subjectEddy detectionen_US
dc.titleAutomatic Detection of Ocean Eddy based on Deep Learning Technique with Attention Mechanismen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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