一种基于CNN-SVR多模态信息融合的GNSS-R风速反演研究  

Research on GNSS-R wind speed retrieval based on CNN-SVR multimodal information fusion

作　　者：胡媛[1] 王宗乾 刘卫[2] 吴林晋 HU Yuan;WANG Zongqian;LIU Wei;WU Linjin(College of Engineering Science and Technology,Shanghai Ocean University,Shanghai 201306,China;Merchant Marine College,Shanghai Maritime University,Shanghai 201306,China)

机构地区：[1]上海海洋大学工程学院,上海201306 [2]上海海事大学商船学院,上海201306

出　　处：《测绘科学》2023年第12期74-83,共10页Science of Surveying and Mapping

基　　金：国家自然科学基金项目(52071199)

摘　　要：针对在全球卫星导航系统反射信号(GNSS-R)技术应用中如何提升海面风速反演总体精度的问题,该文提出一种基于卷积神经网络(CNN)和支持向量机回归(SVR)组合的海面风速反演模型。实验利用气旋全球导航卫星系统(CYGNSS)的L1级数据得到预测风速与参考风速数据相关系数达到了0.89,风速反演的总体均方根误差(RMSE)为1.23m/s,较传统方法的RMSE降低了12.7%~45.3%。In view of the problem that how to improve the overall accuracy of sea surface wind speed retrieval in the application of global navigation satellite system reflectometry(GNSS-R),a combined sea surface wind speed retrieval model based on convolutional neural networks(CNN)and support vector regression(SVR)was proposed.The L1level data of cyclone global navigation satellite system(CYGNSS)was used in the experiment to obtain a correlation coefficient of 0.89between the predicted wind speed and the reference wind speed data,and the overall root mean square error(RMSE)of wind speed retrieval was 1.23m/s,which was 12.7%~45.3%lower than the RMSE of the traditional method.

关 键 词：CYGNSS 风速反演 深度学习 支持向量机回归 

分 类 号：P228.49[天文地球—大地测量学与测量工程]