海洋涡旋智能检测研究进展

徐广珺; 施宇诚; 余洋; 谢华荣; 谢文鸿; 刘婧媛; 林夏艳; 刘宇; 董昌明

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

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海洋学研究 ›› 2024, Vol. 42 ›› Issue (3) : 38-50. DOI: 10.3969/j.issn.1001-909X.2024.03.003

研究综述

海洋涡旋智能检测研究进展

徐广珺 ¹^,² ,
施宇诚 ¹ ,
余洋 ³ ,
谢华荣 ⁴ ,
谢文鸿 ⁵ ,
刘婧媛 ¹ ,
林夏艳 ⁶ ,
刘宇 ⁶ ,
董昌明 ²^,⁴^,^*

作者信息 +

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 +

文章历史 +

摘要

海洋涡旋是一种常见的海洋现象,在全球海洋物质和能量的输运中起着重要作用。随着海洋研究技术手段的不断提升,各类海洋涡旋检测方法应运而生。传统涡旋检测方法应用广泛,但其过度依赖于专家经验设置阈值和持续的人工干预,存在检测误差较大、工作效率低以及全球普适性差等问题,难以适应复杂多变的海洋环境。当前人工智能快速发展,其在海洋涡旋智能检测中能够自动、快速地提取图像深层特征,有效解决海洋现象特征相似度高、几何差异大的问题。该文立足于当前海洋涡旋智能检测的发展现状,从编码器-解码器结构、全卷积神经网络、多尺度上下文方法和注意力机制等方面回顾了不同深度学习方法在海洋涡旋智能检测中的应用,以期为海洋涡旋研究提供一些启示和参考。

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.

导出引用

徐广珺, 施宇诚, 余洋, 等. 海洋涡旋智能检测研究进展[J]. 海洋学研究. 2024, 42(3): 38-50 https://doi.org/10.3969/j.issn.1001-909X.2024.03.003

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

中图分类号： P731.2

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析

[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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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.

本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [1]

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

[23]	JEONG J, HUSSAIN F. On the identification of a vortex[J]. Journal of Fluid Mechanics, 1995, 285: 69-94. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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

本文引用 [1] 摘要

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. 本文引用 [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. 本文引用 [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. 本文引用 [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 L. 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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [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. 本文引用 [1]