Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 15, Issue -, Pages 3868-3876Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2022.3173001
Keywords
Wind; Radar polarimetry; Sea surface; Synthetic aperture radar; Data models; Atmospheric modeling; Transfer learning; Gaofen-3 (GF-3); Inception v3 model; sea surface wind direction; synthetic aperture radar (SAR); wind streak (WS)
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In this study, a transfer learning based convolutional neural network was utilized to identify sea surface wind streaks, achieving high recognition accuracy. The model also showed good performance in retrieving sea surface wind direction.
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 article, 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 is augmented. Compared with the reanalysis data of European Centre for Medium-Range Weather Forecasts, the root-mean-square error of the retrieved wind direction is 9.12 degrees, which further verifies the ability of the training model to identify wind streaks.
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