Correction of Sea Surface Wind Speed Based on SAR Rainfall Grade Classification Using Convolutional Neural Network

The technology of retrieving sea surface wind field from spaceborne synthetic aperture radar (SAR) is increasingly mature. However, the retrieval of the sea surface wind field related to the precipitation effect is still facing challenges, especially the strong precipitation related to extreme weath...

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Veröffentlicht in:IEEE journal of selected topics in applied earth observations and remote sensing 2023-01, Vol.16, p.1-9
Hauptverfasser: Guo, Chaogang, Ai, Weihua, Zhang, Xi, Guan, Yanan, Liu, Yin, Hu, Shensen, Zhao, Xianbin
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container_title IEEE journal of selected topics in applied earth observations and remote sensing
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creator Guo, Chaogang
Ai, Weihua
Zhang, Xi
Guan, Yanan
Liu, Yin
Hu, Shensen
Zhao, Xianbin
description The technology of retrieving sea surface wind field from spaceborne synthetic aperture radar (SAR) is increasingly mature. However, the retrieval of the sea surface wind field related to the precipitation effect is still facing challenges, especially the strong precipitation related to extreme weather such as tropical cyclone will cause the wind speed retrieval error to exceed 10m/s. Semantic segmentation and weak supervision methods have been used for SAR rainfall recognition, but rainfall segmentation is not accurate enough to support the correction of wind field retrieval. In this paper, we propose to use deep learning to classify the rainfall grades in SAR images, and combine the rainfall correction model to improve the retrieval accuracy of sea surface wind speed. To overcome the challenge of limited training samples, the transfer learning method in fine-tune is adopted. Preliminary results demonstrate the effectiveness of this deep learning methodology. The model classifies rain and no-rain images with an accuracy of 96.2%, and classifies rainfall intensity grades with an accuracy of 86.2%. The rainfall correction model with SAR rainfall grade identified by convolution neural network reduces the root mean square error of retrieved wind speed from 3.83 m/s to 1.76 m/s. The combination of SAR rainfall grade recognition and rainfall correction method improves the retrieval accuracy of SAR wind speed, which can further promote the operational application of SAR wind field.
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However, the retrieval of the sea surface wind field related to the precipitation effect is still facing challenges, especially the strong precipitation related to extreme weather such as tropical cyclone will cause the wind speed retrieval error to exceed 10m/s. Semantic segmentation and weak supervision methods have been used for SAR rainfall recognition, but rainfall segmentation is not accurate enough to support the correction of wind field retrieval. In this paper, we propose to use deep learning to classify the rainfall grades in SAR images, and combine the rainfall correction model to improve the retrieval accuracy of sea surface wind speed. To overcome the challenge of limited training samples, the transfer learning method in fine-tune is adopted. Preliminary results demonstrate the effectiveness of this deep learning methodology. The model classifies rain and no-rain images with an accuracy of 96.2%, and classifies rainfall intensity grades with an accuracy of 86.2%. The rainfall correction model with SAR rainfall grade identified by convolution neural network reduces the root mean square error of retrieved wind speed from 3.83 m/s to 1.76 m/s. 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subjects Accuracy
Artificial neural networks
Convolution
Convolutional neural network
correction
Deep learning
Extreme weather
Hurricanes
Image classification
Image segmentation
Inception v3
Machine learning
Methods
Neural networks
Precipitation
Quality
Rain
Rainfall
Rainfall intensity
Recognition
Retrieval
SAR (radar)
Sea surface
Surface wind
Synthetic aperture radar
Synthetic aperture radar (SAR)
Transfer learning
Tropical cyclones
Wind
wind field
Wind speed
title Correction of Sea Surface Wind Speed Based on SAR Rainfall Grade Classification Using Convolutional Neural Network
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