Abstract
Oceanic eddies play an important role in global energy and material transport, and contribute greatly to nutrient and phytoplankton distribution. Deep learning is employed to identify oceanic eddies from sea surface height anomalies data. In order to adapt to segmentation problems for multi-scale oceanic eddies, the pyramid scene parsing network (PSPNet), which is able to satisfy the fusion of semantics and details, is applied as the core algorithm in the eddy detection methods. The results of eddies identified from this artificial intelligence (AI) method are well compared with those from a traditional vector geometry-based (VG) method. More oceanic eddies are detected by the AI algorithm than the VG method, especially for small-scale eddies. Therefore, the present study demonstrates that the AI algorithm is applicable of oceanic eddy detection. It is one of the first few of efforts to bridge AI techniques and oceanography research.
Highlights
Eddies are ubiquitous oceanic features, which play an important role in global energy and material transport, and contribute greatly to nutrient and phytoplankton distribution, enhancing primary production in the ocean [1,2,3,4,5,6,7]
The pyramid scene parsing network (PSPNet) algorithm is applicable for oceanic eddies detection
SSHA data from 2011 to 2014 with the label of eddy information extracted by the vector geometry-based (VG) algorithm in the STCC region are used as the training data, and SSHA data in 2015 are used for the validation dataset
Summary
Eddies are ubiquitous oceanic features, which play an important role in global energy and material transport, and contribute greatly to nutrient and phytoplankton distribution, enhancing primary production in the ocean [1,2,3,4,5,6,7]. Treating sea surface height (SSH) data as a two-dimensional image, deep learning can be applied in oceanic eddies detection. According to the demand for eddy detection, semantic segmentation can be used to identify the oceanic eddies based on the SSH data. Cyclonic and anticyclonic eddies are identified from the SSH data based on the encoder–decoder network U-Net in the classic framework of semantic segmentation. Du et al [24] use deep learning to extract higher-level features and fused multi-scale features to detect oceanic eddies automatically based on synthetic aperture radar images. The Pyramid Scene Parsing Network (PSPNet) is able to fuse semantic and detail features in the different layers and is applicable for oceanic eddy detection due to the diversity in the distribution, sizes and shapes of oceanic eddies.
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