{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T07:15:26Z","timestamp":1770275726084,"version":"3.49.0"},"reference-count":55,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T00:00:00Z","timestamp":1666915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42001274"],"award-info":[{"award-number":["42001274"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mesoscale eddies are typical mesoscale ocean phenomena that exist widely in all oceans and marginal seas around the world, playing important roles in ocean circulation and material transport. They also have important impacts on the safe navigation of ships and underwater acoustic communications. Traditional mesoscale eddy identification methods are subjective and usually depend on parameters that must be pre-defined or adjusted by experts, meaning that their accuracy cannot be guaranteed. With the rise of deep learning, the \u201cyou only look once\u201d (YOLO) series target recognition models have been shown to present certain advantages in eddy detection and recognition. Based on sea level anomaly (SLA) data provided over the past 30 years by the Copernicus Marine Environment Monitoring Service (CMEMS), as well as deep transfer learning, we propose a method for oceanic mesoscale eddy detection and identification based on the \u201cyou only look once level feature\u201d (YOLOF) model. Using the proposed model, the mesoscale eddies in the South China Sea from 1993 to 2021 were detected and identified. Compared with traditional recognition methods, the proposed model had a better recognition effect (with an accuracy of 91%) and avoided the bias associated with subjectively set thresholds; to a certain extent, the model also improved the detection of and the identification speed for mesoscale eddies. The method proposed in this paper not only promotes the development of deep learning in the field of oceanic mesoscale eddy detection and identification, but also provides an effective technical method for the study of mesoscale eddy detection using sea surface height data.<\/jats:p>","DOI":"10.3390\/rs14215411","type":"journal-article","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T09:01:42Z","timestamp":1667120502000},"page":"5411","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Detection and Identification of Mesoscale Eddies in the South China Sea Based on an Artificial Neural Network Model\u2014YOLOF and Remotely Sensed Data"],"prefix":"10.3390","volume":"14","author":[{"given":"Lingjuan","family":"Cao","sequence":"first","affiliation":[{"name":"School of Marine Science and Technology, Tianjin University, Tianjin 300072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dianjun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Tianjin University, Tianjin 300072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuefeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Tianjin University, Tianjin 300072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Quan","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Marine Science and Technology, Tianjin University, Tianjin 300072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1175\/1520-0485(1984)014<0003:OAMOSS>2.0.CO;2","article-title":"Observation and Modeling of Satellite-Sensed Meanders and Eddies off Vancouver Island","volume":"14","author":"Ikeda","year":"1984","journal-title":"J. Phys. Oceanogr."},{"key":"ref_2","unstructured":"Zhao, Y. (2015). Analysis of Sound Propagation in Mesoscale Eddy Environment, Ocean University of China."},{"key":"ref_3","first-page":"518","article-title":"Study on the meso-scale process of the Kuroshio front to the east of Taiwan","volume":"13","author":"Wang","year":"2011","journal-title":"Mar. Sci. Bull."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/nature18640","article-title":"Western boundary currents regulated by interaction between ocean eddies and the atmosphere","volume":"535","author":"Ma","year":"2016","journal-title":"Nature"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1017\/jfm.2016.151","article-title":"Defining Coherent Eddies Objectively from the Vorticity","volume":"795","author":"Haller","year":"2016","journal-title":"J. Fluid Mech."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.dsr.2007.11.005","article-title":"A census of oceanic anticyclonic eddies in the Gulf of Alaska","volume":"55","author":"Henson","year":"2008","journal-title":"Deep Sea Res. Part I Oceanogr. Res. Pap."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1175\/1520-0426(2003)20<772:IOMEFA>2.0.CO;2","article-title":"Identification of Marine Eddies from Altimetric Maps","volume":"20","year":"2003","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_8","first-page":"87\u2013C103","article-title":"Eddies of the Mediterranean Sea: An Altimetric Perspective","volume":"36","year":"2006","journal-title":"J. Phys. Oceanogr."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/S0097-8493(00)00029-7","article-title":"Detection, quantification, and tracking of eddies using streamline geometry","volume":"24","author":"Sadarjoen","year":"2000","journal-title":"Comput. Graph."},{"key":"ref_10","unstructured":"Du, Y. (2017). Automatic Identification of Mesoscale Eddies Based on Ocean Remote Sensing Images and Its Relationship with Fishing Ground Dynamics, Shanghai Ocean University."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lguensat, R., Sun, M., Fablet, R., Tandeo, P., Mason, E., and Chen, G. (2018, January 22\u201327). EddyNet: A Deep Neural Network for Pixel-Wise Classification of Oceanic Eddies. Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518411"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Franz, K., Roscher, R., Milioto, A., Wenzel, S., and Kusche, J. (2018, January 22\u201327). Ocean Eddy Identification and Tracking Using Neural Networks. Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519261"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xu, G., Cheng, C., Yang, W., Xie, W., Kong, L., Hang, R., Ma, F., Dong, C., and Yang, J. (2019). Oceanic Eddy Identification Using an AI Scheme. Remote Sens., 11.","DOI":"10.3390\/rs11111349"},{"key":"ref_14","first-page":"65","article-title":"Ocean mesoscale eddy recognition and visualization based on deep learning","volume":"29","author":"Lu","year":"2020","journal-title":"Comput. Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"715","DOI":"10.3389\/fmars.2021.672334","article-title":"Application of Three Deep Learning Schemes into Oceanic Eddy Detection","volume":"8","author":"Xu","year":"2021","journal-title":"Front. Mar. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 26\u201327). You only look once: Unified, real-time object detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). Yolo9000: Better, faster, stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, Z., Gu, X., Yang, H., Wang, L., Chen, Y., and Wang, D. (2022). Novel YOLOv3 model with structure and hyperparameter optimization for detection of pavement concealed cracks in GPR images. IEEE Transactions on Intelligent Transportation Systems, IEEE.","DOI":"10.1109\/TITS.2022.3174626"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, D., Liu, Z., Gu, X., Wu, W., Chen, Y., and Wang, L. (2022). Automatic Detection of Pothole Distress in Asphalt Pavement Using Improved Convolutional Neural Networks. Remote Sens., 14.","DOI":"10.3390\/rs14163892"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). Efficient det: Scalable and efficient object detection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chen, Q., Wang, Y., Yang, T., Zhang, X., Cheng, J., and Sun, J. (2021, January 20\u201325). You Only Look One-level Feature. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01284"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wu, M. (2021). Identification, Tracking and Characteristic Analysis of Mesoscale Eddies in the South China Sea, Guilin University of Technology.","DOI":"10.1109\/ICCC51575.2020.9345253"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"C11007","DOI":"10.1029\/2005JC003412","article-title":"Surface Kuroshio path in the Luzon Strait area derived from satellite remote sensing data","volume":"111","author":"Yuan","year":"2006","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1712","DOI":"10.1175\/1520-0485(2001)031<1712:TNNOKP>2.0.CO;2","article-title":"The nondeterministic nature of Kuroshio penetration and eddy shedding in the South China Sea","volume":"31","author":"Metzger","year":"2001","journal-title":"J. Phys. Oceanogr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1007\/s10872-006-0076-0","article-title":"Interannual variability of the Kuroshio intrusion in the South China Sea","volume":"62","author":"Caruso","year":"2006","journal-title":"J. Oceanogr."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1007\/s00343-010-9040-3","article-title":"Vertical structure and evolution of the Luzon warn eddy","volume":"28","author":"Chen","year":"2010","journal-title":"Chin. J. Oceanol. Limnol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1016\/S0967-0637(98)00026-0","article-title":"Anticyclonic Rings from the Kuroshio in the South China Sea","volume":"45","author":"Li","year":"1998","journal-title":"Deep Sea Res. Part I Oceanogr. Res. Pap."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xue, H., Chai, F., Pettigrew, N., Xu, D., Shi, M., and Xu, J. (2004). Kuroshio Intrusion and the Circulation in the South China Sea. J. Geophys. Res. Oceans, 109.","DOI":"10.1029\/2002JC001724"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2336","DOI":"10.1175\/JPO-D-14-0009.1","article-title":"A description of local and nonlocal eddy-mean flow interaction in a global eddy-permit-ting state estimate","volume":"44","author":"Chen","year":"2014","journal-title":"J. Phys. Oceanography"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"L13610","DOI":"10.1029\/2007GL029401","article-title":"Anti-cyclonic eddies northwest of Luzon in summer-fall observed by satellite altimeters","volume":"34","author":"Yuan","year":"2007","journal-title":"Geophys. Res. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Nan, F., He, Z., Zhou, H., and Wang, D. (2011). Three long-lived anticyclonic eddies in the northern South China Sea. Geophys. Res. Ocean., 116.","DOI":"10.1029\/2010JC006790"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4185","DOI":"10.1002\/2014JC009957","article-title":"Standing wave modes observed in the South China Sea deep basin","volume":"119","author":"Zheng","year":"2014","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1007\/s10872-008-0063-8","article-title":"Recent progress in studies of the South China Sea circulation","volume":"64","author":"Liu","year":"2008","journal-title":"J. Oceanogr."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1827","DOI":"10.1007\/s11434-012-5269-x","article-title":"Advances in research on the deep South China Sea circulation","volume":"57","author":"Tian","year":"2012","journal-title":"Chin. Sci. Bull."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"150028","DOI":"10.1038\/sdata.2015.28","article-title":"A daily global mesoscale ocean eddy dataset from satellite altimetry","volume":"2","author":"Faghmous","year":"2015","journal-title":"Sci. Data"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7220","DOI":"10.1029\/2018JC014140","article-title":"Up to what extent can we characterize ocean eddies using present-day gridded altimetric products","volume":"123","author":"Amores","year":"2018","journal-title":"J. Geophysics. Res. Ocean"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.5194\/os-12-1067-2016","article-title":"DUACS DT2014: The new multi-mission altimeter dataset reprocessed over 20 years","volume":"12","author":"Pujol","year":"2016","journal-title":"Ocean Sci. Discuss."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"111281","DOI":"10.1016\/j.measurement.2022.111281","article-title":"GPR-based detection of internal cracks in asphalt pavement: A combination method of DeepAugment data and object detection","volume":"197","author":"Liu","year":"2022","journal-title":"Measurement"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"107097","DOI":"10.1016\/j.compeleceng.2021.107097","article-title":"Deep structure learning using feature extraction in trained projection space","volume":"92","author":"CAngermann","year":"2021","journal-title":"Comput. Electr. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"106531","DOI":"10.1016\/j.compeleceng.2019.106531","article-title":"Application of deep learning for autonomous detection and localization of colorectal polyps in wireless colon capsule endoscopy","volume":"81","author":"Nadimi","year":"2020","journal-title":"Comput. Electr. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"317","DOI":"10.5194\/os-7-317-2011","article-title":"Comparison between three implementations of automatic identification algorithms for the quantification and characterization of mesoscale eddies in the South Atlantic Ocean","volume":"7","author":"Souza","year":"2011","journal-title":"Ocean. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Faghmous, J.H., Le, M., Uluyol, M., Kumar, V., and Chatterjee, S. (2013, January 7\u201310). A parameter-free spatio-temporal pattern mining model to catalog global ocean dynamics. Proceedings of the 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA.","DOI":"10.1109\/ICDM.2013.162"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"39","DOI":"10.5194\/os-10-39-2014","article-title":"Enhancing the accuracy of automatic eddy detection and the capability of recognizing the multi-core structures from maps of sea level anomaly","volume":"10","author":"Yi","year":"2014","journal-title":"Ocean. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated residual transformations for deep neural networks. Proceedings of the 2017 IEEE Conference on Computer Vision And Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). Endto-end object detection with transformers. arXiv.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_46","unstructured":"Zhu, B., Wang, J., Jiang, Z., Zong, F., Liu, S., Li, Z., and Sun, J. (2020). Autoassign: Differentiable label assignment for dense object detection. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., and Liu, C. (2018). A Survey on Deep Transfer Learning. arXiv.","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2389","DOI":"10.1109\/ACCESS.2017.2782884","article-title":"Ensemble transfer learning algorithm","volume":"6","author":"Liu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_49","unstructured":"Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., and Darrell, T. (2014). Deep domain confusion: Maximizing for domain invariance. arXiv."},{"key":"ref_50","unstructured":"Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014). How transferable are features in deep neural networks?. arXiv."},{"key":"ref_51","unstructured":"Long, M., Cao, Z., Wang, J., and Jordan, M.I. (2017). Domain adaptation with randomized multilinear adversarial networks. arXiv."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Xu, R., Liu, J., and Xu, J. (2018). Extraction of High-Precision Urban Impervious Surfaces from Sentinel-2 Multispectral Imagery via Modified Linear Spectral Mixture Analysis. Sensors, 18.","DOI":"10.3390\/s18092873"},{"key":"ref_53","unstructured":"Hao, Y. (2017). Research on Ocean Mesoscale Eddy Detection Algorithm Based on Convolution Neural Network. [Master\u2019s Thesis, Shandong University of Science and Technology]."},{"key":"ref_54","first-page":"18","article-title":"Mesoscale eddy detection technology based on deep learning and its application in sound field","volume":"35","author":"Pengfei","year":"2020","journal-title":"Ocean. Inf."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"106336","DOI":"10.1109\/ACCESS.2019.2931781","article-title":"A streampath-based RCNN approach to ocean eddy detection","volume":"7","author":"Bai","year":"2019","journal-title":"IEEE Access"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5411\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:04:51Z","timestamp":1760144691000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5411"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,28]]},"references-count":55,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14215411"],"URL":"https:\/\/doi.org\/10.3390\/rs14215411","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,28]]}}}