| 基于SVM回归模型的SAR图像有效波高估测 |
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| 引用本文： | 高东,刘永信,孟俊敏,贾永君,范陈清.基于SVM回归模型的SAR图像有效波高估测[J].海洋学报(英文版),2018,37(3):103-110. |
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| 作者姓名： | 高东 刘永信 孟俊敏 贾永君 范陈清 |
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| 作者单位： | 内蒙古大学电子信息工程学院, 内蒙古, 呼和浩特, 010020;国家海洋局第一海洋研究所, 山东, 青岛, 266061,内蒙古大学电子信息工程学院, 内蒙古, 呼和浩特, 010020,国家海洋局第一海洋研究所, 山东, 青岛, 266061,国家卫星海洋应用中心, 北京, 100081,国家海洋局第一海洋研究所, 山东, 青岛, 266061 |
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| 基金项目： | 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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| 摘 要： | 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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| 关 键 词： | ASAR波模式 支持向量机 有效波高 |
| 收稿时间： | 2017/1/17 0:00:00 |
Estimating significant wave height from SAR imagery based on an SVM regression model |
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| GAO Dong,LIU Yongxin,MENG Junmin,JIA Yongjun and FAN Chenqing.Estimating significant wave height from SAR imagery based on an SVM regression model[J].Acta Oceanologica Sinica,2018,37(3):103-110. |
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| Authors: | GAO Dong LIU Yongxin MENG Junmin JIA Yongjun and FAN Chenqing |
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| Institution: | 1.College of Electronic and Information Engineering, Inner Mongolia University, Hohhot 010020, China;The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China2.College of Electronic and Information Engineering, Inner Mongolia University, Hohhot 010020, China3.The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China4.National Satellite Ocean Application Service, State Oceanic Administration, Beijing 100081, China |
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| 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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| Keywords: | advanced synthetic aperture radar wave mode support vector machine significant wave height |
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