Volume 43 Issue 9
Sep.  2024
Turn off MathJax
Article Contents
Haipeng Yu, Xiaoliang Chu, Guang Yuan. Estimation of peak wave period from surface texture motion in videos[J]. Acta Oceanologica Sinica, 2024, 43(9): 136-144. doi: 10.1007/s13131-024-2359-y
Citation: Haipeng Yu, Xiaoliang Chu, Guang Yuan. Estimation of peak wave period from surface texture motion in videos[J]. Acta Oceanologica Sinica, 2024, 43(9): 136-144. doi: 10.1007/s13131-024-2359-y

Estimation of peak wave period from surface texture motion in videos

doi: 10.1007/s13131-024-2359-y
Funds:  The Key R&D Program of Shandong Province under contract No. 2023CXPT101.
More Information
  • Corresponding author: E-mail: xlchu@ouc.edu.cn
  • Received Date: 2024-03-19
  • Accepted Date: 2024-06-24
  • Available Online: 2024-08-01
  • Publish Date: 2024-09-01
  • Wave information retrieval from videos captured by a single camera has been increasingly applied in marine observation. However, when the camera observes ocean waves at low grazing angles, the accurate extraction of wave information from videos will be affected by the interference of the fine ripples on the sea surface. To solve this problem, this study develops a method for estimating peak wave periods from videos captured at low grazing angles. The method extracts the motion of the sea surface texture from the video and obtains the peak wave period via the spectral analysis. The calculation results captured from real-world videos are compared with those obtained from X-band radar inversion and tracking buoy movement, with maximum deviations of 8% and 14%, respectively. The analysis of the results shows that the peak wave period of the method has good stability. In addition, this paper uses a pinhole camera model to convert the displacement of the texture from pixel height to actual height and performs moving average filtering on the displacement of the texture, thus conducting a preliminary exploration of the inversion of significant wave height. This study helps to extend the application of sea surface videos.
  • loading
  • Afzal M S, Kumar L. 2022. Propagation of waves over a rugged topography. Journal of Ocean Engineering and Science, 7(1): 14–28, doi: 10.1016/j.joes.2021.04.004
    Ahn S, Haas K A, Neary V S. 2020. Wave energy resource characterization and assessment for coastal waters of the United States. Applied Energy, 267: 114922, doi: 10.1016/j.apenergy.2020.114922
    Alberello A, Bennetts L G, Onorato M, et al. 2022. Three-dimensional imaging of waves and floes in the marginal ice zone during a cyclone. Nature Communications, 13(1): 4590, doi: 10.1038/s41467-022-32036-2
    Almar R, Bergsma E W J, Catalan P A, et al. 2021. Sea state from single optical images: A methodology to derive wind-generated ocean waves from cameras, drones and satellites. Remote Sensing, 13(4): 679, doi: 10.3390/rs13040679
    Ardhuin F, Stopa J E, Chapron B, et al. 2019. Observing sea states. Frontiers in Marine Science, 6: 124, doi: 10.3389/fmars.2019.00124
    Arefin M A, Saeed M A, Akbar M A, et al. 2022. Analytical behavior of weakly dispersive surface and internal waves in the ocean. Journal of Ocean Engineering and Science, 7(4): 305–312, doi: 10.1016/j.joes.2021.08.012
    Battjes J A, Groenendijk H W. 2000. Wave height distributions on shallow foreshores. Coastal Engineering, 40(3): 161–182, doi: 10.1016/S0378-3839(00)00007-7
    Bay H, Ess A, Tuytelaars T, et al. 2008. Speeded-up robust features (SURF). Computer Vision and Image Understanding, 110(3): 346–359, doi: 10.1016/j.cviu.2007.09.014
    Benetazzo A. 2006. Measurements of short water waves using stereo matched image sequences. Coastal Engineering, 53(12): 1013–1032, doi: 10.1016/j.coastaleng.2006.06.012
    Bergamasco F, Benetazzo A, Yoo J, et al. 2021. Toward real-time optical estimation of ocean waves’ space-time fields. Computers & Geosciences, 147: 104666, doi: 10.1016/j.cageo.2020.104666
    Bergamasco F, Torsello A, Sclavo M, et al. 2017. WASS: An open-source pipeline for 3D stereo reconstruction of ocean waves. Computers & Geosciences, 107: 28–36, doi: 10.1016/j.cageo.2017.07.001
    Blenkinsopp C E, Bayle P M, Martins K, et al. 2022. Wave runup on composite beaches and dynamic cobble berm revetments. Coastal Engineering, 176: 104148, doi: 10.1016/j.coastaleng.2022.104148
    Brodtkorb P A, Johannesson P, Lindgren G, et al. 2000. WAFO—A Matlab toolbox for analysis of random waves and loads. Paper presented at The Tenth International Offshore and Polar Engineering Conference. Washington, DC, USA. ISOPE 2000-GFC-02
    Chaturvedi S K. 2019. A case study of tsunami detection system and ocean wave imaging mechanism using radar. Journal of Ocean Engineering and Science, 4(3): 203–210, doi: 10.1016/j.joes.2019.04.005
    Cui He, Chen Jianyu, Cao Zhenyi, et al. 2022. A novel multi-candidate multi-correlation coefficient algorithm for GOCI-derived sea-surface current vector with OSU tidal model. Remote Sensing, 14(18): 4625, doi: 10.3390/rs14184625
    Davison S, Benetazzo A, Barbariol F, et al. 2022. Space-time statistics of extreme ocean waves in crossing sea states. Frontiers in Marine Science, 9: 1002806, doi: 10.3389/fmars.2022.1002806
    Falcon E, Mordant N. 2022. Experiments in surface gravity-capillary wave turbulence. Annual Review of Fluid Mechanics, 54(1): 1–25, doi: 10.1146/annurev-fluid-021021-102043
    Goda Y. 2009. A performance test of nearshore wave height prediction with CLASH datasets. Coastal Engineering, 56(3): 220–229, doi: 10.1016/j.coastaleng.2008.07.003
    Goncalves H, Corte-Real L, Goncalves J A. 2011. Automatic image registration through image segmentation and SIFT. IEEE Transactions on Geoscience and Remote Sensing, 49(7): 2589–2600, doi: 10.1109/TGRS.2011.2109389
    Guimarães P V, Ardhuin F, Bergamasco F, et al. 2020. A data set of sea surface stereo images to resolve space-time wave fields. Scientific Data, 7(1): 145, doi: 10.1038/s41597-020-0492-9
    Hao Yang, Tang Tao, Gao Chunhai. 2023. Train distance estimation in turnout area based on monocular vision. Sensors, 23(21): 8778, doi: 10.3390/s23218778
    Holman R A, Stanley J. 2007. The history and technical capabilities of Argus. Coastal Engineering, 54(6/7): 477–491, doi: 10.1016/j.coastaleng.2007.01.003
    Horn B K P, Schunck B G. 1981. Determining optical flow. Artificial Intelligence, 17(1–3): 185–203, doi: 10.1016/0004-3702(81)90024-2
    Kim B O. 2005. Photography aided determination of video camera orientation in coastal environments. Journal of Coastal Research, (42): 352–362
    Kim B O, Cho H Y. 2005. Image processing for video images of buoy motion. Ocean Science Journal, 40(4): 213–220, doi: 10.1007/BF03023521
    Kim B O, Cho H Y, Lim D I, et al. 2008. Nearshore wave measurement using single-video images of buoy motions. Journal of Coastal Research, 246: 1481–1486, doi: 10.2112/07-0850.1
    Kim J I, Hyun C U, Han H, et al. 2019. Evaluation of matching costs for high-quality sea-ice surface reconstruction from aerial images. Remote Sensing, 11(9): 1055, doi: 10.3390/rs11091055
    Kim M, Lee S, Hong J W. 2022. Empirical estimation of the breaker index using a stereo camera system. Ocean Engineering, 265: 112522, doi: 10.1016/j.oceaneng.2022.112522
    Li Jiangxia, Pan Shunqi, Chen Yongping, et al. 2022. Assessment of combined wind and wave energy in the tropical cyclone affected region: An application in China seas. Energy, 260: 125020, doi: 10.1016/j.energy.2022.125020
    Lowe D G. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2): 91–110, doi: 10.1023/B:VISI.0000029664.99615.94
    Malila M P, Thomson J, Breivik Ø, et al. 2022. On the Groupiness and intermittency of oceanic whitecaps. Journal of Geophysical Research: Oceans, 127(1): e2021JC017938, doi: 10.1029/2021JC017938
    Malliouri D I, Memos C D, Soukissian T H, et al. 2021. Assessing failure probability of coastal structures based on probabilistic representation of sea conditions at the structures’ location. Applied Mathematical Modelling, 89: 710–730, doi: 10.1016/j.apm.2020.08.001
    Osorio A F, Montoya-Vargas S, Cartagena C A, et al. 2019. Virtual BUOY: A video-based approach for measuring near-shore wave peak period. Computers & Geosciences, 133: 104302, doi: 10.1016/j.cageo.2019.07.006
    Palmsten M L, Brodie K L. 2022. The coastal imaging research network (CIRN). Remote Sensing, 14(3): 453, doi: 10.3390/rs14030453
    Pan Hailang, Gao Peilin, Zhou Huicheng, et al. 2020. Roughness analysis of sea surface from visible images by texture. IEEE Access, 8: 46448–46458, doi: 10.1109/ACCESS.2020.2978638
    Perugini E, Soldini L, Palmsten M L, et al. 2019. Linear depth inversion sensitivity to wave viewing angle using synthetic optical video. Coastal Engineering, 152: 103535, doi: 10.1016/j.coastaleng.2019.103535
    Pierson Jr W J. 1954. An interpretation of the observable properties of ‘sea’ waves in terms of the energy spectrum of the Gaussian Record. Eos, Transactions American Geophysical Union, 35(5): 747–757, doi: 10.1029/TR035i005p00747
    Rattanapitikon W. 2008. Verification of significant wave representation method. Ocean Engineering, 35(11/12): 1259–1270, doi: 10.1016/j.oceaneng.2008.03.008
    Rattanapitikon W, Shibayama T. 2013. Verification and extension of goda formulas for computing representative wave heights transformation. Coastal Engineering Journal, 55(3): 1350009, doi: 10.1142/S0578563413500095
    Spencer L, Shah M. 2004. Water video analysis. In: Proceedings of International Conference on Image Processing. Singapore: IEEE, 2705–2708, doi: 10.1109/ICIP.2004.1421662
    Spencer L, Shah M, Guha R K. 2006. Determining scale and sea state from water video. IEEE Transactions on Image Processing, 15(6): 1525–1535, doi: 10.1109/TIP.2006.871102
    Stilwell Jr D. 1969. Directional energy spectra of the sea from photographs. Journal of Geophysical Research, 74(8): 1974–1986, doi: 10.1029/JB074i008p01974
    Tessendorf J. 2001. Simulating ocean water. In: Simulating nature: realistic and interactive techniques. SIGGRAPH, 3-1–3-26
    Ti Zilong, Zhang Mingjin, Li Yongle, et al. 2019. Numerical study on the stochastic response of a long-span sea-crossing bridge subjected to extreme nonlinear wave loads. Engineering Structures, 196: 109287, doi: 10.1016/j.engstruct.2019.109287
    Vieira M, Guimarães P V, Violante-Carvalho N, et al. 2020. A low-cost stereo video system for measuring directional wind waves. Journal of Marine Science and Engineering, 8(11): 831, doi: 10.3390/jmse8110831
    Villas Bôas A B, Ardhuin F, Ayet A, et al. 2019. Integrated observations of global surface winds, currents, and waves: Requirements and challenges for the next decade. Frontiers in Marine Science, 6: 425, doi: 10.3389/fmars.2019.00425
    Wu Lichung, Doong Dongjiing, Lai Jianwu. 2022. Influences of nononshore winds on significant wave height estimations using coastal X-band radar images. IEEE Transactions on Geoscience and Remote Sensing, 60: 1–11, doi: 10.1109/TGRS.2021.3077903
    Ye Yuanxin, Bruzzone L, Shan Jie, et al. 2019. Fast and robust matching for multimodal remote sensing image registration. IEEE Transactions on Geoscience and Remote Sensing, 57(11): 9059–9070, doi: 10.1109/TGRS.2019.2924684
    Yoo S, Kim N. 2023. Coarse alignment methodology of point cloud based on camera position/orientation estimation model. Journal of Imaging, 9(12): 279, doi: 10.3390/jimaging9120279
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(17)  / Tables(3)

    Article Metrics

    Article views (119) PDF downloads(4) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return