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

GAO Dong; LIU Yongxin; MENG Junmin; JIA Yongjun; FAN Chenqing

doi:10.1007/s13131-018-1203-7

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Article Navigation > Acta Oceanologica Sinica > 2018 > 37(3): 103-110

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

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

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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

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.
- advanced synthetic aperture radar wave mode,
- support vector machine,
- significant wave height

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