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 |
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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. |
doi_str_mv | 10.1109/JSTARS.2022.3224438 |
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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. 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.</description><identifier>ISSN: 1939-1404</identifier><identifier>EISSN: 2151-1535</identifier><identifier>DOI: 10.1109/JSTARS.2022.3224438</identifier><identifier>CODEN: IJSTHZ</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>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</subject><ispartof>IEEE journal of selected topics in applied earth observations and remote sensing, 2023-01, Vol.16, p.1-9</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-8cff8934086b3b0002e468bbb7b54e4e195cb1b8902d6c5291f9a6f341f5615d3</citedby><cites>FETCH-LOGICAL-c408t-8cff8934086b3b0002e468bbb7b54e4e195cb1b8902d6c5291f9a6f341f5615d3</cites><orcidid>0000-0001-7907-6363 ; 0000-0002-9514-4458 ; 0000-0002-1538-7469</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,782,786,866,2104,27931,27932</link.rule.ids></links><search><creatorcontrib>Guo, Chaogang</creatorcontrib><creatorcontrib>Ai, Weihua</creatorcontrib><creatorcontrib>Zhang, Xi</creatorcontrib><creatorcontrib>Guan, Yanan</creatorcontrib><creatorcontrib>Liu, Yin</creatorcontrib><creatorcontrib>Hu, Shensen</creatorcontrib><creatorcontrib>Zhao, Xianbin</creatorcontrib><title>Correction of Sea Surface Wind Speed Based on SAR Rainfall Grade Classification Using Convolutional Neural Network</title><title>IEEE journal of selected topics in applied earth observations and remote sensing</title><addtitle>JSTARS</addtitle><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.</description><subject>Accuracy</subject><subject>Artificial neural networks</subject><subject>Convolution</subject><subject>Convolutional neural network</subject><subject>correction</subject><subject>Deep learning</subject><subject>Extreme weather</subject><subject>Hurricanes</subject><subject>Image classification</subject><subject>Image segmentation</subject><subject>Inception v3</subject><subject>Machine learning</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Precipitation</subject><subject>Quality</subject><subject>Rain</subject><subject>Rainfall</subject><subject>Rainfall intensity</subject><subject>Recognition</subject><subject>Retrieval</subject><subject>SAR (radar)</subject><subject>Sea surface</subject><subject>Surface wind</subject><subject>Synthetic aperture radar</subject><subject>Synthetic aperture radar (SAR)</subject><subject>Transfer learning</subject><subject>Tropical cyclones</subject><subject>Wind</subject><subject>wind field</subject><subject>Wind speed</subject><issn>1939-1404</issn><issn>2151-1535</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNo9UV1r3DAQFKWBXNP8grwI-uyrVh-29Xg1bZoSWjgn5FFIshR0da2rZLf031d3DkGwuywzs7sahG6AbAGI_Pitf9jt-y0llG4ZpZyz9g3aUBBQgWDiLdqAZLICTvglepfzgZCaNpJtUOpiSs7OIU44etw7jfsleW0dfgrTgPujcwP-pHOJBdLv9nivw-T1OOLbpAeHu1HnHHyw-izymMP0jLs4_YnjcuroEX93Szqn-W9MP9-ji0LP7volX6HHL58fuq_V_Y_bu253X1lO2rlqrfetZKWuDTOEEOp43RpjGiO44w6ksAZMKwkdaiuoBC917RkHL2oQA7tCd6vuEPVBHVP4pdM_FXVQ50ZMz0qnOdjRKc-INwOXnGvLWz_IRpcHBsoChDS-aH1YtY4p_l5cntUhLqnclhVteFPD6esLiq0om2LOyfnXqUDUySi1GqVOaPViVGHdrKzgnHtlSFkzITn7D_kmjrI</recordid><startdate>20230101</startdate><enddate>20230101</enddate><creator>Guo, Chaogang</creator><creator>Ai, Weihua</creator><creator>Zhang, Xi</creator><creator>Guan, Yanan</creator><creator>Liu, Yin</creator><creator>Hu, Shensen</creator><creator>Zhao, Xianbin</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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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. 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.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/JSTARS.2022.3224438</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-7907-6363</orcidid><orcidid>https://orcid.org/0000-0002-9514-4458</orcidid><orcidid>https://orcid.org/0000-0002-1538-7469</orcidid><oa>free_for_read</oa></addata></record> |
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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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