Oceanic Eddy Identification Using an AI Scheme

Xu, Guangjun; Cheng, Cheng; Yang, Wenxian; Xie, Wenhong; Kong, Lingmei; Hang, Renlong; Ma, Furong; Dong, Changming; Yang, Jianguo

doi:10.3390/rs11111349

Cited by 62 publications

(35 citation statements)

References 30 publications

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“…Recently, Xu et al [31] used PSPNet and vector geometry-based (VG) algorithms to develop the oceanic eddy AI algorithm. However, the accuracy of train set is limited by VG algorithm, which makes this AI algorithm omit eddies deviating from symmetrical structure.…”

Section: Outline Of Our Methodsmentioning

confidence: 99%

Oceanic Mesoscale Eddy Detection Method Based on Deep Learning

Duo

Wang

2019

Remote Sensing

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Oceanic mesoscale eddies greatly influence energy and matter transport and acoustic propagation. However, the traditional detection method for oceanic mesoscale eddies relies too much on the threshold value and has significant subjectivity. The existing machine learning methods are not mature or purposeful enough, as their train set lacks authority. In view of the above problems, this paper constructs a mesoscale eddy automatic identification and positioning network—OEDNet—based on an object detection network. Firstly, 2D image processing technology is used to enhance the data of a small number of accurate eddy samples annotated by marine experts to generate the train set. Then, the object detection model with a deep residual network, and a feature pyramid network as the main structure, is designed and optimized for small samples and complex regions in the mesoscale eddies of the ocean. Experimental results show that the model achieves better recognition compared to the traditional detection method and exhibits a good generalization ability in different sea areas.

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Section: Outline Of Our Methodsmentioning

confidence: 99%

Oceanic Mesoscale Eddy Detection Method Based on Deep Learning

Duo

Wang

2019

Remote Sensing

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“…It uses multiple SLA data to search for mesoscale eddies with small samples and in complex regions. Xu and Cheng proposed an artificial intelligence algorithm for eddy detection based on PSPNet and the VG algorithm [ 28 ]. Limited by the VG algorithm, the accuracy remains on a similar level as the geometry-based algorithm.…”

Section: Related Workmentioning

confidence: 99%

DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning

Liu

Zhou

Wen

2020

Sensors

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Oceanic eddy is a common natural phenomenon that has large influence on human activities, and the measurement and detection of offshore eddies are significant for oceanographic research. The previous classical detecting methods, such as the Okubo–Weiss algorithm (OW), vector geometry algorithm (VG), and winding angles algorithm (WA), not only depend on expert’s experiences to set an accurate threshold, but also need heavy calculations for large detection regions. Differently from the previous works, this paper proposes a deep eddy detection neural network with pixel segmentation skeleton on high frequency radar (HFR) data, namely, the deep eddy detection network (DEDNet). An offshore eddy detection dataset is firstly constructed, which has origins from the sea surface current data measured by two HFR systems on the South China Sea. Then, a spatial globally optimum and strong detail-distinguishing pixel segmentation network is presented to automatically detect and localize offshore eddies in a flow chart. An eddy detection network based on fully convolutional networks (FCN) is also presented for comparison with DEDNet. Experimental results show that DEDNet performs better than the FCN-based eddy detection network and is competitive with the classical statistics-based methods.

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“…With the rapid development of artificial intelligence (AI), due to its applicability across diverse fields and the ability to consider non-linearities in complex physical mechanisms, AI techniques have been widely applied in the field of marine sciences. These range from the automatic detection and prediction of mesoscale eddies (Zeng et al, 2015;Xu et al, 2019), El Niño-Southern Oscillation, Arctic sea ice density, and sea surface temperature prediction (Aparna et al, 2018;Kim et al, 2018Kim et al, , 2020Ham et al, 2019;Zheng et al, 2020). Wave forecasting has also been attempted through AI techniques though this is mostly a single-point wave forecasting.…”

Section: Introductionmentioning

confidence: 99%

ConvLSTM-Based Wave Forecasts in the South and East China Seas

Zhou

Xie

et al. 2021

Front. Mar. Sci.

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Numerical wave models have been developed for the wave forecast in last two decades; however, it faces challenges in terms of the requirement of large computing resources and improvement of accuracy. Based on a convolutional long short-term memory (ConvLSTM) algorithm, this paper establishes a two-dimensional (2D) significant wave height (SWH) prediction model for the South and East China Seas trained by WaveWatch III (WW3) reanalysis data. We conduct 24-h predictions under normal and extreme conditions, respectively. Under the normal wave condition, for 6-, 12-, and 24-h forecasting, their correlation coefficients are 0.98, 0.93, and 0.83, and the mean absolute percentage errors are 15, 29, and 61%. Under the extreme condition (typhoon), for 6 and 12 h, their correlation coefficients are 0.98 and 0.94, and the mean absolute percentage errors are 19 and 40%, which is better than the model trained by all the data. It is concluded that the ConvLSTM can be applied to the 2D wave forecast with high accuracy and efficiency.

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Oceanic Eddy Identification Using an AI Scheme

Cited by 62 publications

References 30 publications

Oceanic Mesoscale Eddy Detection Method Based on Deep Learning

Oceanic Mesoscale Eddy Detection Method Based on Deep Learning

DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning

ConvLSTM-Based Wave Forecasts in the South and East China Seas

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