Recent developments in AI-based oceanic eddy identification

XU Guangjun; SHI Yucheng; YU Yang; XIE Huarong; XIE Wenhong; LIU Jingyuan; LIN Xiayan; LIU Yu; DONG Changming

doi:10.3969/j.issn.1001-909X.2024.03.003

PDF(2744 KB)

Journal of Marine Sciences ›› 2024, Vol. 42 ›› Issue (3) : 38-50. DOI: 10.3969/j.issn.1001-909X.2024.03.003

Recent developments in AI-based oceanic eddy identification

XU Guangjun ¹^,² ,
SHI Yucheng ¹ ,
YU Yang ³ ,
XIE Huarong ⁴ ,
XIE Wenhong ⁵ ,
LIU Jingyuan ¹ ,
LIN Xiayan ⁶ ,
LIU Yu ⁶ ,
DONG Changming ²^,⁴^,^*

Author information +

History +

Abstract

Ocean eddies are prevalent oceanic phenomenon that play a crucial role in the global transportation of oceanic materials and energy. Although traditional methods for detecting ocean eddies are widely used, they suffer from significant drawbacks such as excessive reliance on expert-set thresholds, continuous manual intervention, large detection errors, low efficiency, and poor global applicability, making it difficult to adapt to the complex and variable marine environment. Currently, the rapid development of artificial intelligence (AI) presents a promising solution for the intelligent detection of ocean eddies. AI can automatically and rapidly extract deep features from images, effectively address the challenges posed by the high similarity in oceanic phenomenon features and significant geometric variability. This paper provides an overview of AI-based oceanic eddy identification methods based on different deep learning methods, focuses on coder-decoder structure, fully convolutional neural network, multi-scale context method and attention mechanism, and aims to provide valuable insights and references for future ocean eddy research.

Key words

oceanic eddy / artificial intelligence / feature detection / deep learning / coder-decoder structure / fully convolutional neural network / multi-scale context method / attention mechanism

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

XU Guangjun , SHI Yucheng , YU Yang , et al . Recent developments in AI-based oceanic eddy identification[J]. Journal of Marine Sciences. 2024, 42(3): 38-50 https://doi.org/10.3969/j.issn.1001-909X.2024.03.003

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis

[1]	MORROW R, LE TRAON P Y. Recent advances in observing mesoscale ocean dynamics with satellite altimetry[J]. Advances in Space Research, 2012, 50(8): 1062-1076. Cited in this article [1]

[2]	WANG H Z, LIU D, ZHANG W M, et al. Characterizing the capability of mesoscale eddies to carry drifters in the northwest Pacific[J]. Journal of Oceanology and Limnology, 2020, 38(6): 1711-1728. Cited in this article [1]

[3]	DONG C M, MCWILLIAMS J C, LIU Y, et al. Global heat and salt transports by eddy movement[J]. Nature Commu-nications, 2014, 5: 3294. Cited in this article [2]

[4]	JI J L, DONG C M, ZHANG B, et al. An oceanic eddy statistical comparison using multiple observational data in the Kuroshio extension region[J]. Acta Oceanologica Sinica, 2017, 36(3): 1-7. Cited in this article [1]

[5]	FRENGER I, MÜNNICH M, GRUBER N, et al. Southern Ocean eddy phenomenology[J]. Journal of Geophysical Research: Oceans, 2015, 120(11): 7413-7449. Cited in this article [1]

[6]	CHELTON D B, SCHLAX M G, SAMELSON R M, et al. Global observations of large oceanic eddies[J]. Geophysical Research Letters, 2007, 34(15): L15606. Cited in this article [1]

[7]	董昌明. 海洋涡旋探测与分析[M]. 北京: 科学出版社, 2015. DONG C M. Oceanic eddy detection and analysis[M]. Beijing: Science Press, 2015. Cited in this article [1]

[8]	FRENGER I, GRUBER N, KNUTTI R, et al. Imprint of Southern Ocean eddies on winds, clouds and rainfall[J]. Nature Geoscience, 2013, 6: 608-612. Cited in this article [1]

[9]	GAUBE P, CHELTON D B, SAMELSON R M, et al. Satellite observations of mesoscale eddy-induced Ekman pumping[J]. Journal of Physical Oceanography, 2015, 45(1): 104-132. Cited in this article [1]

[10]	GAUBE P, BARCELÓ C, MCGILLICUDDY D J Jr, et al. The use of mesoscale eddies by juvenile loggerhead sea turtles (Caretta caretta) in the southwestern Atlantic[J]. PLoS One, 2017, 12(3): e0172839. Cited in this article [1]

[11]	李佳讯, 张韧, 陈奕德, 等. 海洋中尺度涡建模及其在水声传播影响研究中的应用[J]. 海洋通报, 2011, 30(1):37-46. LI J X, ZHANG R, CHEN Y D, et al. Ocean mesoscale eddy modeling and its application in studying the effect on underwater acoustic propagation[J]. Marine Science Bulletin, 2011, 30(1): 37-46. Cited in this article [1]

[12]	DONG C M, LIU Y, LUMPKIN R, et al. A scheme to identify loops from trajectories of oceanic surface drifters: An application in the Kuroshio extension region[J]. Journal of Atmospheric and Oceanic Technology, 2011, 28(9): 1167-1176. Cited in this article [2]

[13]

NENCIOLI

, DONG

C M

, DICKEY

, et al. A vector geometry-based eddy detection algorithm and its application to a high-resolution numerical model product and high-frequency radar surface velocities in the southern California bight[J]. Journal of Atmospheric and Oceanic Technology, 2010, 27(3): 564-579.

Cited in this article [2]

[14]	ZHANG C H, LI H L, LIU S T, et al. Automatic detection of oceanic eddies in reanalyzed SST images and its application in the East China Sea[J]. Science China Earth Sciences, 2015, 58(12): 2249-2259. Cited in this article [1]

[15]	SUN W J, LIU Y, CHEN G X, et al. Three-dimensional properties of mesoscale cyclonic warm-core and anticyclonic cold-core eddies in the South China Sea[J]. Acta Oceanologica Sinica, 2021, 40(10): 17-29. Cited in this article [1]

[16]	XU G J, YANG J S, DONG C M, et al. Statistical study of submesoscale eddies identified from synthetic aperture radar images in the Luzon Strait and adjacent seas[J]. International Journal of Remote Sensing, 2015, 36(18): 4621-4631. Cited in this article [1]

[17]	JI Y X, XU G J, DONG C M, et al. Submesoscale eddies in the East China Sea detected from SAR images[J]. Acta Oceanologica Sinica, 2021, 40(3): 18-26. Cited in this article [1]

[18]	OKUBO A. Horizontal dispersion of floatable particles in the vicinity of velocity singularities such as convergences[J]. Deep Sea Research and Oceanographic Abstracts, 1970, 17(3): 445-454. Cited in this article [1]

[19]	WEISS J. The dynamics of enstrophy transfer in two-dimensional hydrodynamics[J]. Physica D: Nonlinear Phenomena, 1991, 48(2/3): 273-294. Cited in this article [1]

[20]	HUNT J C R. Vorticity and vortex dynamics in complex turbulent flows[J]. Transactions of the Canadian Society for Mechanical Engineering, 1987, 11(1): 21-35. Cited in this article [1]

[21]	LIU C Q, WANG Y Q, YANG Y, et al. New omega vortex identification method[J]. Science China Physics, Mechanics & Astronomy, 2016, 59(8): 684711. Cited in this article [1]

[22]	CHONG M S, PERRY A E, CANTWELL B J. A general classification of three-dimensional flow fields[J]. Physics of Fluids A: Fluid Dynamics, 1990, 2(5): 765-777. Cited in this article [1]

[23]	JEONG J, HUSSAIN F. On the identification of a vortex[J]. Journal of Fluid Mechanics, 1995, 285: 69-94. Cited in this article [1]

[24]	DOGLIOLI A M, BLANKE B, SPEICH S, et al. Tracking coherent structures in a regional ocean model with wavelet analysis: Application to Cape Basin eddies[J]. Journal of Geophysical Research: Oceans, 2007, 112(C5): C05043. Cited in this article [1]

[25]	CHAIGNEAU A, GIZOLME A, GRADOS C. Mesoscale eddies off Peru in altimeter records: Identification algorithms and eddy spatio-temporal patterns[J]. Progress in Oceano-graphy, 2008, 79(2/3/4): 106-119. Cited in this article [1]

[26]	FRANZ K, ROSCHER R, MILIOTO A, et al. Ocean eddy identification and tracking using neural networks[C]//2018 IEEE International Geoscience and Remote Sensing Sympo-sium(IGARSS). IEEE, 2018: 6887-6890. Cited in this article [3]

[27]	LGUENSAT R, SUN M, FABLET R, et al. EddyNet: A deep neural network for pixel-wise classification of oceanic eddies[C]//2018 IEEE International Geoscience and Remote Sensing Symposium(IGARSS). IEEE, 2018: 1764-1767. Cited in this article [1]

[28]

X F

, LIU

, ZHENG

, et al. Deep-learning-based information mining from ocean remote-sensing imagery[J]. National Science Review, 2020, 7(10): 1584-1605.

https://doi.org/10.1093/nsr/nwaa047

https://www.ncbi.nlm.nih.gov/pubmed/34691490

Cited in this article [1] Abstract

With the continuous development of space and sensor technologies during the last 40 years, ocean remote sensing has entered into the big-data era with typical five-V (volume, variety, value, velocity and veracity) characteristics. Ocean remote-sensing data archives reach several tens of petabytes and massive satellite data are acquired worldwide daily. To precisely, efficiently and intelligently mine the useful information submerged in such ocean remote-sensing data sets is a big challenge. Deep learning-a powerful technology recently emerging in the machine-learning field-has demonstrated its more significant superiority over traditional physical- or statistical-based algorithms for image-information extraction in many industrial-field applications and starts to draw interest in ocean remote-sensing applications. In this review paper, we first systematically reviewed two deep-learning frameworks that carry out ocean remote-sensing-image classifications and then presented eight typical applications in ocean internal-wave/eddy/oil-spill/coastal-inundation/sea-ice/green-algae/ship/coral-reef mapping from different types of ocean remote-sensing imagery to show how effective these deep-learning frameworks are. Researchers can also readily modify these existing frameworks for information mining of other kinds of remote-sensing imagery.© The Author(s) 2020. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd.

[29]	DUO Z J, WANG W K, WANG H Z. Oceanic mesoscale eddy detection method based on deep learning[J]. Remote Sensing, 2019, 11(16): 1921. Cited in this article [1]

[30]	SANTANA O J, HERNÁNDEZ-SOSA D, SMITH R N. Oceanic mesoscale eddy detection and convolutional neural network complexity[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 113: 102973. Cited in this article [1]

[31]	张盟, 杨玉婷, 孙鑫, 等. 基于深度卷积网络的海洋涡旋检测模型[J]. 南京航空航天大学学报, 2020, 52(5):708-713. ZHANG M, YANG Y T, SUN X, et al. Ocean eddy detection model based on deep convolution neural network[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2020, 52(5): 708-713. Cited in this article [1]

[32]

刘启明, 杨树国, 赵莉. 基于深度卷积神经网络的海洋多目标涡旋检测方法[J]. 青岛科技大学学报:自然科学版, 2022, 43(4):120-126.

LIU

Q M

, YANG

S G

, ZHAO

. Ocean multi-eddy detection method based on deep convolution neural network[J]. Journal of Qingdao University of Science and Technology: Natural Science Edition, 2022, 43(4): 120-126.

Cited in this article [1]

[33]	LIU Y J, LI X F, REN Y B. A deep learning model for oceanic mesoscale eddy detection based on multi-source remote sensing imagery[C]//2020 IEEE International Geoscience and Remote Sensing Symposium(IGARSS). IEEE, 2020: 6762-6765. Cited in this article [1]

[34]	沈飙, 陈扬, 杨琛, 等. 海洋科学中尺度涡的计算机视觉检测和分析方法[J]. 数据与计算发展前沿, 2020, 2(6):30-41. SHEN B, CHEN Y, YANG C, et al. Computer vision detection and analysis of mesoscale eddies in marine science[J]. Frontiers of Data & Computing, 2020, 2(6): 30-41. Cited in this article [1]

[35]	LIU Y J, ZHENG Q A, LI X F. Characteristics of global ocean abnormal mesoscale eddies derived from the fusion of sea surface height and temperature data by deep learning[J]. Geophysical Research Letters, 2021, 48(17): e94772. Cited in this article [3]

[36]	CAO L J, ZHANG D J, ZHANG X F, et al. Detection and identification of mesoscale eddies in the South China Sea based on an artificial neural network model—YOLOF and remotely sensed data[J]. Remote Sensing, 2022, 14(21): 5411. Cited in this article [1]

[37]	YAN Z F, CHONG J S, ZHAO Y W, et al. Multifeature fusion neural network for oceanic phenomena detection in SAR images[J]. Sensors, 2019, 20(1): 210. Cited in this article [1]

[38]	DU Y L, SONG W, HE Q, et al. Deep learning with multi-scale feature fusion in remote sensing for automatic oceanic eddy detection[J]. Information Fusion, 2019, 49: 89-99. Cited in this article [1]

[39]	ZHANG D, GADE M, ZHANG J W. SAR eddy detection using mask-RCNN and edge enhancement[C]//2020 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), September 26-October 2, 2020, Waikoloa, HI, USA. IEEE, 2020: 1604-1607. Cited in this article [1]

[40]	贾翊文, 荆文龙, 杨骥, 等. 基于深度学习的SAR影像海洋涡旋检测算法对比分析[J]. 海洋科学进展, 2024, 42(1):137-148. JIA Y W, JING W L, YANG J, et al. Comparative analysis of ocean eddy detection algorithms based on deep learning in SAR images[J]. Advances in Marine Science, 2024, 42(1): 137-148. Cited in this article [1]

[41]	LIU F Y, ZHOU H, HUANG W M, et al. Cross-domain submesoscale eddy detection neural network for HF radar[J]. Remote Sensing, 2021, 13(13): 2441. Cited in this article [3]

[42]	XU G J, CHENG C, YANG W X, et al. Oceanic eddy identification using an AI scheme[J]. Remote Sensing, 2019, 11: 1349. Cited in this article [3]

[43]	XU G J, XIE W H, DONG C M, et al. Application of three deep learning schemes into oceanic eddy detection[J]. Frontiers in Marine Science, 2021, 8: 672334. Cited in this article [1]

[44]	HANG R L, LI G, XUE M, et al. Identifying oceanic eddy with an edge-enhanced multiscale convolutional network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 9198-9207. Cited in this article [1]

[45]	ZHAO Y X, FAN Z L, LI H T, et al. SymmetricNet: End-to-end mesoscale eddy detection with multi-modal data fusion[J]. Frontiers in Marine Science, 2023, 10: 1174818. Cited in this article [1]

[46]	杜艳玲, 王丽丽, 黄冬梅, 等. 融合密集特征金字塔的改进R²CNN海洋涡旋自动检测[J]. 智能系统学报, 2023, 18(2):341-351. DU Y L, WANG L L, HUANG D M, et al. Improved R²CNN ocean eddy automatic detection with a dense feature pyramid[J]. CAAI Transactions on Intelligent Systems, 2023, 18(2): 341-351. Cited in this article [1]

[47]	FAN Z L, ZHONG G Q. SymmetricNet: A mesoscale eddy detection method based on multivariate fusion data[Z/OL]. arXiv, 2019: 1909. 13411. https://arxiv.org/abs/1909.13411v1. https://arxiv.org/abs/1909.13411v1 Cited in this article [1]

[48]	XIA L H, CHEN G, CHEN X Y, et al. Submesoscale oceanic eddy detection in SAR images using context and edge association network[J]. Frontiers in Marine Science, 2022, 9: 1023624. Cited in this article [3]

[49]	ZHANG Y Y, LIU N, ZHANG Z Y, et al. Detection of Bering Sea slope mesoscale eddies derived from satellite altimetry data by an attention network[J]. Remote Sensing, 2022, 14(19): 4974. Cited in this article [3]

[50]	SAIDA S J, SAHOO S P, ARI S. Deep convolution neural network based semantic segmentation for ocean eddy detection[J]. Expert Systems with Applications, 2023, 219: 119646. Cited in this article [1]

[51]	ZHAO N, HUANG B X, YANG J, et al. Oceanic eddy identification using pyramid split attention U-net with remote sensing imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 1-5. Cited in this article [1]

[52]	杜艳玲, 吴天宇, 陈括, 等. 融合上下文和注意力的海洋涡旋小目标检测[J]. 中国图象图形学报, 2023, 28(11):3509-3519. DU Y L, WU T Y, CHEN K, et al. Small object detection for ocean eddies using contextual information and attention mechanism[J]. Journal of Image and Graphics, 2023, 28(11): 3509-3519. Cited in this article [1]