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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Acknowledgments
The authors thank the ESA for providing the ASAR data and the European Centre for Medium-range Weather Forecasts (ECMWF) for providing the reanalysis data (http://www.ecmwf. int/en/research/climate-reanalysis/browse-reanalysis-datasets). The authors also thank NDBC for providing the measured buoy data (http://www.ndbc.noaa.gov/).
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Foundation item: The National Key Research and Development Program of China under contract Nos 2016YFA0600102 and 2016YFC1401007; the National Natural Science Youth Foundation of China under contract No.61501130; the Natural Science Foundation of China under contract No. 41406207.
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Gao, D., Liu, Y., Meng, J. et al. Estimating significant wave height from SAR imagery based on an SVM regression model. Acta Oceanol. Sin. 37, 103–110 (2018). https://doi.org/10.1007/s13131-018-1203-7
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DOI: https://doi.org/10.1007/s13131-018-1203-7