Abstract
Nonlinear ocean waves have a significant impact on the functioning of several offshore activities. Predicting the internal ocean waves plays a crucial role on submarine and ship operations. Data assimilation is a mechanism in which data observed is interpreted, processed and adapted. The existing works for estimating the future atmospheric condition are highly dependent on the exact initial state, which mostly differ from the observation. This paper proposes modelling of internal ocean waves using automatic internal wave detection and data assimilation. Ensemble Kalman filtering method is used to model ocean waves. The proposed system is focused on satellite images. The images are pre-processed for speckle noise using adaptive filters. Enhanced residual network is used for edge detection. Unlike the existing edge detection methods that have high complexity, this enhanced residual network works with low complexity and makes a direct mapping between the input wave image and wave edge. Finally, the potential edges of the internal wave are detected and adapted using ensemble Kalman filter. Adaptive thresholding technique is used to determine the appropriate threshold to segregate objects from background. The proposed enhanced edge detection model is compared w.r.t to the parameters weighted cross-entropy loss function, accuracy and root mean squared error with canny edge detection and proved to be better. The detection of internal wave is demonstrated, and the accuracy of the approach is 91% with low RMSE when compared to existing works.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability statement
The data sets generated and analysed during the current study are available in the [European Space agency, ERS-1 and ERS-2 SAR] repository, [http://www.esa.int/Applications/Observing_the_Earth/ERS_1_and_2SuluSea].
References
Galanis, G., Emmanouil, G., Chu, P.C., et al.: A new methodology for the extension of the impact of data assimilation on ocean wave prediction. Ocean Dyn. 59, 523–535 (2009). https://doi.org/10.1007/s10236-009-0191-8
Moore, A.M., Martin, M.J., Akella, S., Arango, H.G., Balmaseda, M., Bertino, L., Ciavatta, S., Cornuelle, B., Cummings, J., Frolov, S., Lermusiaux, P., Oddo, P., Oke, P.R., Storto, A., Teruzzi, A., Vidard, A., Weaver, A.T.: Synthesis of ocean observations using data assimilation for operational, real-time and reanalysis systems: a more complete picture of the state of the ocean. Front. Mar. Sci. 6, 90 (2019). https://doi.org/10.3389/fmars.2019.00090
Aragh, S., Nwogu, O., Lyzenga, D.: Improved Estimation of Ocean Wave Fields From Marine Radars Using Data Assimilation Techniques. Paper presented at the The Eighteenth International Offshore and Polar Engineering Conference, Vancouver, Canada (2008)
Caires, S., Marseille, G.J., Verlaan, M., Stoffelen, A.: North Sea wave analysis using data assimilation and mesoscale model forcing winds. J. Waterway Port Coast. Ocean Eng. (2018). https://doi.org/10.1061/(ASCE)WW.1943-5460.0000439
European Space agency, ERS 1 and ERS2 SAR. http://www.esa.int/Applications/Observing_the_Earth/ERS_1_and_2Sulu. Sea, Last accessed May 5th 2021
Rodenas, J.A., Garello, R.: Internal wave detection and location in SAR images using wavelet transform. IEEE Trans. Geosci. Remote Sens. 36(5), 1494–1507 (1998)
Divya, C., Vasavi, S., Sarma, A.S.: Ocean internal wave detection from sar images using particle swarm optimization. In: 2020 Third International Conference on Advances in Electronics, Computers and Communications (ICAECC), Bengaluru, India, pp. 1–6 (2020). https://doi.org/10.1109/ICAECC50550.2020.9339511
Simonin, D., Tatnall, A.R., Robinson, I.S.: The automated detection and recognition of internal waves. Int. J. Remote Sens. 30(17), 4581–4598 (2009). https://doi.org/10.1080/01431160802621218
WienerNorbert, J.: Extrapolation, Interpolation, and Smoothing of Stationary Time Series. Wiley, New York (1949)
Kumar, A., Manjunatha Chari, K.: Noise reduction using modified wiener filter in digital hearing aid for speech signal enhancements. J Intell Anal 29(1), 1360–1378 (2019). https://doi.org/10.1515/jisys-2017-0509
Al-Amaren, A., Ahmad, M.O., Swamy, M.N.S.: RHN: a residual holistic neural network for edge detection. IEEE Access 9, 74646–74658 (2021). https://doi.org/10.1109/ACCESS.2021.3078411
Saulter, A.N., Bunney, C., King, R.R., Waters, J.: An application of NEMOVAR for regional wave model data assimilation. Front. Mar. Sci. 7, 579834 (2020). https://doi.org/10.3389/fmars.2020.579834
Ponte, R.M., Carson, M., Cirano, M., Domingues, C.M., Jevrejeva, S., Marcos, M., Zhang, X.: Towards comprehensive observing and modeling systems for monitoring and predicting regional to coastal sea level. Front. Mar. Sci. 6(437), 1–25 (2019). https://doi.org/10.3389/fmars.2019.00437
Mohaghegh, F., Alam, M.R., Murthy, J.: Rapid Phase-Resolved Prediction of Nonlinear Dispersive Waves Using Machine Learning. arXiv: 2007.15250
Sai Pravallika, M., Naga Varun, B., Vasavi, S., Sandeep, N., Jaya Priya, M., Sashikanth Sarma, A.: Ocean wave modeling from satellite images using data assimilation. In: Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol. 288. Springer, Singapore. https://doi.org/10.1007/978-981-16-5120-5_7
Zagoruyko, S., Komodakis, N.: Wide Residual Networks, Computer Vision and Pattern Recognition (2016). arXiv:1605.07146
Vasavi, S., Priyadarshini, N.K., Vardhan, K.H.: Invariant feature based darknet architecture for moving object classification. IEEE Sensors J. (2020). https://doi.org/10.1109/JSEN.2020.3007883
Murphy, K.: Machine Learning: A Probabilistic Perspective. MIT, Cambridge (2012)
Ke, Qu., Fengqin, Z., Wenhua, S.: A novel method for internal wave monitoring based on expansion of the sound speed profile. Acta Oceanol. Sin. 38(4), 183–189 (2019). https://doi.org/10.1007/s13131-019-1422-6
Bocaniova, S.A., Ullmanna, C., Rinkea, K., Lambb, K.G., Boehreraa, B.: Internal waves and mixing in a stratified reservoir: Insights from three-dimensional modeling. Limnologica 49, 52–67 (2014)
Zhang, X., Li, X.: Combination of satellite observations and machine learning method for internal wave forecast in the Sulu and Celebes Seas. IEEE Trans. Geosci. Remote Sens. 59(4), 2822–2832 (2021)
Cheng, S.B., Quilodrán-Casas, C., Ouala, S., Farchi, A., Liu, C., Tandeo, P., Fablet, R., Lucor, D., Iooss, B., Brajard, J., Xiao, D.H., Janjic, T., Ding, W.P., Guo, Y.K., Carrassi, A., Bocquet, M., Arcucci, R.: Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review. IEEE/CAA J. Autom. Sinica 10(6), 1361–1387 (2023). https://doi.org/10.1109/JAS.2023.123537
Houghton, I.A., Penny, S.G., Hegermiller, C., Cesaretti, M., Teicheira, C., Smit, P.B.: Ensemble-based data assimilation of significant wave height from Sofar Spotters and satellite altimeters with a global operational wave model. Ocean Model 183, 102200 (2023)
Agushaka, J.O., Ezugwu, A.E., Abualigah, L.: Gazelle optimization algorithm: a novel nature-inspired metaheuristic optimizer. Neural Comput. Appl. 35, 4099–4131 (2023). https://doi.org/10.1007/s00521-022-07854-6
Roger Labbe, K.: Bayesian Filters in Python, Github, pp. 498–506 (2014). https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
Siva Reddy: A Study on Global Ocean Analysis from Ocean Data Assimilation System and its Sensitivity to Observations and Forcing Fields, pp. 17–18. https://doi.org/10.13140/RG.2.1.4459.4326
Ming, Y., Li, H., He, X.: Contour completion without region segmentation. IEEE Trans. Image Process. 25(8), 3597–3611 (2016)
Zhang, X., Zheng, Q., Li, X.: Satellite data-driven internal solitary wave forecast based on machine learning techniques. In: Li, X., Wang, F. (eds.), Artificial Intelligence Oceanography. Springer, Singapore (2023). https://doi.org/10.1007/978-981-19-6375-9_4
Shao, Qi., Li, W., Han, G., Hou, G., Liu, S., Gong, Y., Ping, Qu.: A Deep Learning Model for Forecasting Sea Surface Height Anomalies and Temperatures in the South China Sea (2021). https://doi.org/10.1029/2021JC017515
Xue, Q., Hu, H., Bai, Y., et al.: Underwater image enhancement algorithm based on color correction and contrast enhancement. Vis. Comput. (2023). https://doi.org/10.1007/s00371-023-03117-0
Zardoua, Y., Astito, A., Boulaala, M.: A survey on horizon detection algorithms for maritime video surveillance: advances and future techniques. Vis. Comput. 39, 197–217 (2023). https://doi.org/10.1007/s00371-021-02321-0
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Vasavi, S., Pravallika, M.S., Varun, B.N. et al. Residual network-based ocean wave modelling from satellite images using ensemble Kalman filter. Vis Comput 40, 6359–6368 (2024). https://doi.org/10.1007/s00371-023-03169-2
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00371-023-03169-2