| GAO Dong,LIU Yongxin,MENG Junmin,JIA Yongjun,FAN Chenqing. 2018. Estimating significant wave height from SAR imagery based on an SVM regression model. Acta Oceanologica Sinica, 37(3):103－110 |
| Estimating significant wave height from SAR imagery based on an SVM regression model |
| 基于SVM回归模型的SAR图像有效波高估测 |
| Received:January 17, 2017 |
| DOI：10.1007/s13131-018-1203-7 |
| Key words:advanced synthetic aperture radar wave mode support vector machine significant wave height |
| 中文关键词: ASAR波模式 支持向量机 有效波高 |
| 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. |
| Author Name | Affiliation | E-mail | | GAO Dong | College of Electronic and Information Engineering, Inner Mongolia University, Hohhot 010020, China
The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China | | | LIU Yongxin | College of Electronic and Information Engineering, Inner Mongolia University, Hohhot 010020, China | | | MENG Junmin | The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China | | | JIA Yongjun | National Satellite Ocean Application Service, State Oceanic Administration, Beijing 100081, China | | | FAN Chenqing | The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China | fanchenqing@fio.org.cn |
|
| Hits: 1508 |
| Download times: 1563 |
| 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. |
| 中文摘要: |
| 本文提出了基于支持向量机（SVM）回归模型从ASAR波模式数据估测SWH新方法。该方法根据SAR图像的平均后向散射系数、图像方差及图谱分解参数与海浪SWH之间的非线性关系建立SVM回归模型，模型的输入为ASAR数据提取的图像特征参数，输出为欧洲中期天气预报中心（ECMWF）提供的SWH数据。提取图像特征参数建立训练样本集并作为粒子群参数优化方法的输入数据，寻找建立SVM回归模型的最优参数。最后对SVM进行训练得到提取SWH的SVM回归模型。SVM回归模型估测结果与ECMWF再分析数据和浮标数据分别进行了比对，SWH的均方根差分别为0.34m和0.48m，相关度分别为0.94和0.81。结果表明，基于SVM回归模型的SAR SWH提取是一种有效的方法，其优点在于可以将SAR数据作为独立数据源进行海浪观测，避免了海浪谱的复杂求解过程。 |
|
HTML
View Full Text
View/Add Comment Download reader |
| Close |
|
|
|
