Abstract
Recent years have witnessed the increase in applications of artificial intelligence (AI) into the detection of oceanic features. Oceanic eddies, ubiquitous in the global ocean, are important in the transport of materials and energy. A series of eddy detection schemes based on oceanic dynamics have been developed while the AI-based eddy identification scheme starts to be reported in literature. In the present study, to find out applicable AI-based schemes in eddy detection, three AI-based algorithms are employed in eddy detection, including the pyramid scene parsing network (PSPNet) algorithm, the DeepLabV3+ algorithm and the bilateral segmentation network (BiSeNet) algorithm. To justify the AI-based eddy detection schemes, the results are compared with one dynamic-based eddy detection method. It is found that more eddies are identified using the three AI-based methods. The three methods’ results are compared in terms of the numbers, sizes and lifetimes of detected eddies. In terms of eddy numbers, the PSPNet algorithm identifies the largest number of ocean eddies among the three AI-based methods. In terms of eddy sizes, the BiSeNet can find more large-scale eddies than the two other methods, because the Spatial Path is introduced into the algorithm to avoid destroying the eddy edge information. Regarding eddy lifetimes, the DeepLabV3+ cannot track longer lifetimes of ocean eddies.
Highlights
Oceanic eddies are ubiquitous in the global ocean
In order to further discuss the artificial intelligence (AI)-based results, the eddies identified by the AI-based algorithms and by the VG algorithm in the STCC region during 2015 are compared with mesoscale eddy trajectory atlas product (Ver 2.0), which is obtained from AVISO+2
Sea surface height anomaly (SSHA) data from 2011 to 2014 in the STCC region, labeled with eddy information detected by the VG algorithm, are employed for training, and SSHA data in 2015 used for validation
Summary
Oceanic eddies are ubiquitous in the global ocean. They play an important role in material and energy transport, and global climate changes. Oceanic mesoscale eddies contribute significantly to horizontal heat and salt transports (Dong et al, 2014; Moreau et al, 2017; Patel et al, 2019, 2020). Deep learning schemes have been used in oceanic eddies detection during the past few years. Lguensat et al (2017) firstly applied a deep learning algorithm based on the encoder-decoder network to oceanic eddies detection in the classic framework of the semantic segmentation. Based on synthetic aperture radar images, deep learning was applied to automatically detect oceanic eddies according to the extracted higher-level features and fused multi-scale features (Du et al, 2019). Xu et al (2019) applied the pyramid scene parsing network (PSPNet) to identify oceanic eddies and find that the PSPNet has great advantage in the detection of small-scale eddies. Based on synthetic aperture radar images, deep learning was applied to automatically detect oceanic eddies according to the extracted higher-level features and fused multi-scale features (Du et al, 2019). Xu et al (2019) applied the pyramid scene parsing network (PSPNet) to identify oceanic eddies and find that the PSPNet has great advantage in the detection of small-scale eddies. Duo et al (2019) proposed an Ocean Eddy Detection Net (OEDNet) based on an object detection network to recognize the eddy field by enhancing the accurate small sample data to obtain the training dataset
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