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Volume 37 Issue 3
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Article Navigation >  Acta Oceanologica Sinica  > 2018  >  37(3): 103-110
HE Hailun, XU Yao. Wind-wave hindcast in the Yellow Sea and the Bohai Sea from the year 1988 to 2002[J]. Acta Oceanologica Sinica, 2016, 35(3): 46-53. doi: 10.1007/s13131-015-0786-5
Citation: GAO Dong, LIU Yongxin, MENG Junmin, JIA Yongjun, FAN Chenqing. Estimating significant wave height from SAR imagery based on an SVM regression model[J]. Acta Oceanologica Sinica, 2018, 37(3): 103-110. doi: 10.1007/s13131-018-1203-7
shu

Estimating significant wave height from SAR imagery based on an SVM regression model

doi: 10.1007/s13131-018-1203-7
  • 1.

    College of Electronic and Information Engineering, Inner Mongolia University, Hohhot 010020, China;The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China

  • 2.

    College of Electronic and Information Engineering, Inner Mongolia University, Hohhot 010020, China

  • 3.

    The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China

  • 4.

    National Satellite Ocean Application Service, State Oceanic Administration, Beijing 100081, China

  • Received Date: 2017-01-17
    Abstract
  • A new method for estimating significant wave height (SWH) from advanced synthetic aperture radar (ASAR) wave mode data based on a support vector machine (SVM) regression model is presented. The model is established based on a nonlinear relationship between σ0, the variance of the normalized SAR image, SAR image spectrum spectral decomposition parameters and ocean wave SWH. The feature parameters of the SAR images are the input parameters of the SVM regression model, and the SWH provided by the European Centre for Medium-range Weather Forecasts (ECMWF) is the output parameter. On the basis of ASAR matching data set, a particle swarm optimization (PSO) algorithm is used to optimize the input kernel parameters of the SVM regression model and to establish the SVM model. The SWH estimation results yielded by this model are compared with the ECMWF reanalysis data and the buoy data. The RMSE values of the SWH are 0.34 and 0.48 m, and the correlation coefficient is 0.94 and 0.81, respectively. The results show that the SVM regression model is an effective method for estimating the SWH from the SAR data. The advantage of this model is that SAR data may serve as an independent data source for retrieving the SWH, which can avoid the complicated solution process associated with wave spectra.
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  • Alpers W, Brümmer B. 1994. Atmospheric boundary layer rolls observed by the synthetic aperture radar aboard the ERs-1 satellite. Journal of Geophysical Research, 99(C6): 12613-12621
    Behravan I, Dehghantanha O, Zahiri S H, et al. 2016. An optimal SVM with feature selection using multiobjective PSO. Journal of Optimization, 2016: 6305043
    Elbisy M S. 2015. Support vector machine and regression analysis to predict the field hydraulic conductivity of sandy soil. KSCE Journal of Civil Engineering, 19(7): 2307-2316
    Fadel S, Ghoniemy S, Abdallah M, et al. 2016. Investigating the effect of different kernel functions on the performance of SVM for recognizing Arabic characters. International Journal of Advanced Computer Science and Applications, 7(1): 446-450
    Gade M, Alpers W, Hühnerfuss H, et al. 1998. Imaging of biogenic and anthropogenic ocean surface films by the multifrequency/multipolarization SIR-C/X-SAR. Journal of Geophysical Research, 103(C9): 18851-18866
    Hasselmann S, Brüning C, Hasselmann K, et al. 1996. An improved algorithm for the retrieval of ocean wave spectra from synthetic aperture radar image spectra. Journal of Geophysical Research, 101(C7): 16615-16629
    Hasselmann K, Hasselmann S. 1991. On the nonlinear mapping of an ocean wave spectrum into a synthetic aperture radar image spectrum and its inversion. Journal of Geophysical Research, 96(C6): 10713-10729
    Kennedy J, Eberhart R. 1995. Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks. Perth, WA: IEEE, 1942-1948
    Li Xiaoming, Lehner S, Bruns T. 2011. Ocean wave integral parameter measurements using envisat ASAR wave mode data. IEEE Transactions on Geoscience and Remote Sensing, 49(1): 155-174
    Mastenbroek C, de Valk C F. 2000. A semiparametric algorithm to retrieve ocean wave spectra from synthetic aperture radar. Journal of Geophysical Research, 105(C2): 3497-3516
    Melsheimer C, Alpers W, Gade M. 1998. Investigation of multifrequency/multipolarization radar signatures of rain cells over the ocean using SIR-C/X-SAR data. Journal of Geophysical Research, 103(C9): 18867-18884
    Nisha M G, Pillai G N. 2013. Nonlinear model predictive control with relevance vector regression and particle swarm optimization. Journal of Control Theory and Applications, 11(4): 563-569
    Park K A, Woo H J, Lee E Y, et al. 2013. Validation of significant wave height from satellite altimeter in the seas around Korea and error characteristics. Korean Journal of Remote Sensing, 29(6): 631-644
    Schulz-Stellenfleth J, König T, Lehner S. 2007. An empirical approach for the retrieval of integral ocean wave parameters from synthetic aperture radar data. Journal of Geophysical Research, 112(C3), doi: 10.1029/2006JC003970
    Schulz-Stellenfleth J, König T, Lehner S. 2006. Retrieval of integral ocean wave parameters from SAR data using an empirical approach. In: Proceedings of SEASAR 2006. 1–6
    Schulz-Stellenfleth J, Lehner S. 2004. Measurement of 2-D sea surface elevation fields using complex synthetic aperture radar data. IEEE Transactions on Geoscience and Remote Sensing, 42(6): 1149-1160
    Singh G, Kumar V, Vekataraman G, et al. 2007. Snow porosity estimation using advanced synthetic aperture radar single look complex data analysis and its effects on backscattering coefficient. Journal of Applied Remote Sensing, 1(1): 013522
    Suganyadevi M V, Babulal C K, Kalyani S. 2016. Assessment of voltage stability margin by comparing various support vector regression models. Soft Computing, 20(2): 807-818
    Sun Jian, Guan Changlong. 2006. Parameterized first-guess spectrum method for retrieving directional spectrum of swell-dominated waves and huge waves from SAR images. Chinese Journal of Oceanology and Limnology, 24(1): 12-20
    Vapnik V N. 1998. Statistical Learning Theory. New York: Wiley
    Wang Cheng. 2014. Optimization of SVM method with RBF kernel. Applied Mechanics and Materials, 496-500: 2306-2310
    Wang Long, Bai Yanping. 2014. Research on prediction of air quality index based on NARX and SVM. Applied Mechanics and Materials, 602-605: 3580-3584
    Xu Qifa, Jiang Cuixia, He Yaoyao. 2016. An exponentially weighted quantile regression via SVM with application to estimating multiperiod VaR. Statistical Methods & Applications, 25(2): 285-320
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