Sea Surface Wind Direction Retrieval Based on Convolution Neural Network and Wavelet Analysis

Abstract

Sea surface wind streak is one of many geophysical phenomena in synthetic aperture radar (SAR) images, which is often used to obtain sea surface wind direction. At present, the recognition of wind streaks mainly depends on artificial experience, and the recognition efficiency and accuracy are not high. In this study, the transfer learning based convolutional neural network architecture of Inception v3 was introduced to the recognition of sea surface wind streaks. Four categories of geophysical phenomena imaged by gaofen-3 (GF-3) SAR from 2019 to 2020 were chosen for retraining of the full pre-retrained Inception v3 model. Then, we use the retrained model to identify the wind streak of GF-3 in 2018 and use it to retrieve the sea surface wind direction. The results show that the transfer learning method is effective. The recognition accuracy of the model can reach 92.0% and 95.2% after data augmented. Compared with the reanalysis data of european centre for medium-range weather forecasts (ECMWF), the root mean square error of the retrieved wind direction is 9.12, which further verifies the ability of the training model to identify wind streaks.