Abstract

In this work, we used the convolutional neural network (CNN) method to invert sea surface height (SSH) from the Global Navigation Satellite System (GNSS) delayed Doppler map (DDM) data during 2009–2017 and compared the CNN inversion data with those obtained from traditional simple random forest (RF) method. SSH observations from the OSTM/Jason-2 satellite were used to judge the merits of the two methods. The results show that both methods yield good SSH inversion results, but when the training set is 9000, the root mean square errors of the SSH inversion results based on the CNN and the RF method are 16.78 and 15.96 respectively; as the training set increases above 9000, the accuracy of the CNN method is significantly better than that of the RF method. This suggests that SSH inversion based on the CNN method will become more advantageous as more data become available.

中文翻译：

---

深度学习在GNSS数据集海面高度估计中的应用

摘要

在这项工作中，我们使用卷积神经网络（CNN）方法从全球导航卫星系统（GNSS）延迟多普勒图（DDM）数据中反演2009-2017年的海面高度（SSH），并将CNN反演数据与那些数据进行比较。从传统的简单随机森林（RF）方法获得。OSTM/Jason-2 卫星的 SSH 观测结果被用来判断这两种方法的优点。结果表明，两种方法都得到了较好的SSH反演结果，但当训练集为9000时，基于CNN和RF方法的SSH反演结果均方根误差分别为16.78和15.96；当训练集增加到9000以上时，CNN方法的准确率明显优于RF方法。这表明，随着更多数据的出现，基于 CNN 方法的 SSH 反演将变得更加有利。