针对合成孔径雷达(SAR)海浪有效波高反演方法开展研究,提出了基于超限学习机(ELM)模型的SAR海浪有效波高经验反演方法。通过对ENVISAT ASAR波模式数据和ECMWF再分析数据进行数据时空匹配得到SAR图像与海浪有效波高的匹配数据集,分别在大匹配数据集和小匹配数据集两种情况下对SAR海洋有效波高反演算法进行经验建模,并与业务化CWAVE算法进行了对比验证。结果表明:大匹配数据集下,所提经验模型的精度为0.87,反演精度总体略逊于CWAVE算法(0.91),但在模型训练效率方面,所提经验算法(0.022 s)要优于CWAVE算法(0.514 s);在小匹配数据集下,所提经验算法反演精度为0.59,模型效率为0.008 s,均远优于CWAVE算法(-0.38和0.318 s)。基于ELM模型可以实现小匹配数据集下SAR海浪有效波高的较高精度反演。

An empirical method for synthetic aperture radar (SAR) sea significant wave height (SWH) inversion based on the extreme learning machine (ELM) model is proposed in this study. Spatial-temporal-matched ENVISAT ASAR image and ECMWF reanalysis sea SWH dataset are collected and analyzed to establish the empirical method. Two cases of mass- and less-matched datasets are used to investigate the capability of the ELM model to establish the empirical relationship from the SAR image parameters to wave SWH parameters. In addition, the CWAVE method is compared with established empirical method as a reference. Results show that the training precision of empirical method is 0.87 in the case of mass-matched dataset, which is slightly worse than that of the CWAVE algorithm (0.91). However, in terms of the method training efficiency, the empirical method (0.022 s) behaves better than the CWAVE algorithm (0.514 s). Moreover, in the case of less-matched dataset, the training accuracy of empirical method is 0.59 and its training efficiency is 0.008 s, which is much better than the CWAVE algorithm (-0.38 and 0.318 s), revealing that the ELM-based empirical method can achieve high retrieval precision of SAR wave SWH results under less-matched dataset.