The Fusion of Physical, Textural, and Morphological Information in SAR Imagery for Hurricane Wind Speed Retrieval Based on Deep Learning
文献类型：期刊论文
| 作者 | Mu, Shanshan2,3 ; Li, Xiaofeng1,3; Wang, Haoyu2,3
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| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2022 |
| 卷号 | 60页码:13 |
| ISSN号 | 0196-2892 |
| 关键词 | Hurricanes Synthetic aperture radar Wind speed Radar polarimetry Sea surface Sea measurements Data models Deep learning hurricane wind synthetic aperture radar (SAR) |
| DOI | 10.1109/TGRS.2022.3174824 |
| 通讯作者 | Li, Xiaofeng(xiaofeng.li@ieee.org) |
| 英文摘要 | This study developed a deep-learning-based model to retrieve sea surface hurricane winds from synthetic aperture radar (SAR) imagery. We introduce the essential idea, residual learning, of the Residual Net into the artificial neural network and design a deep cross-layer concatenation network (DCCN). The model inputs include SAR measured physical parameters in backscattering energy, the texture feature represented by the gray level co- occurrence matrix (GLCM), and the morphological hurricane feature. We collected 45 satellite SAR images from Sentinel-1 (S1) over hurricane conditions. These images were divided into 39 and six for model development and independent testing. A total of 16 127 wind samples acquired from 39 SAR images and simultaneously measured by the stepped frequency microwave radiometer (SFMR) were collected as model tuning datasets, among which 80% and 20% were used for training and validation. Our validation results show that the deep-learning-based model achieved a correlation coefficient (CORR) and root-mean-square error (RMSE) of 0.98 and 1.72 m/s for wind speeds up to 75 m/s. We further applied the model to six independent SAR images. The model significantly outperformed two existing geophysical algorithms and one backpropagation neural network (BPNN) algorithm with the RMSE is 2.61 m/s and a CORR of 0.95. Moreover, statistical analysis in different wind speed regimes indicates that our model shows a stable performance improvement than comparable algorithms. The RMSE decreases 10%-70%, especially the reduction of RMSE is more than 45% at high wind speed (>42 m/s). Furthermore, adding an independent rainfall estimate to the deep-learning model further enhanced the wind retrieval algorithm. |
| 资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDA19060101] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB42040401] ; Key Research and Development Project of Shandong Province[2019JZZY010102] ; Key Deployment Project of the Centre for Ocean Mega-Science through the CAS Programs[COMS2019R02] ; Key Deployment Project of the Centre for Ocean Mega-Science through the CAS Programs[Y9KY04101L] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000804647900031 |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/179482]  |
| 专题 | 海洋研究所_海洋环流与波动重点实验室 |
| 通讯作者 | Li, Xiaofeng |
| 作者单位 | 1.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China 2.Univ Chinese Acad Sci, Sch Oceanog, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Oceanog, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China |
| 推荐引用方式 GB/T 7714 | Mu, Shanshan,Li, Xiaofeng,Wang, Haoyu. The Fusion of Physical, Textural, and Morphological Information in SAR Imagery for Hurricane Wind Speed Retrieval Based on Deep Learning[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60:13. |
| APA | Mu, Shanshan,Li, Xiaofeng,&Wang, Haoyu.(2022).The Fusion of Physical, Textural, and Morphological Information in SAR Imagery for Hurricane Wind Speed Retrieval Based on Deep Learning.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60,13. |
| MLA | Mu, Shanshan,et al."The Fusion of Physical, Textural, and Morphological Information in SAR Imagery for Hurricane Wind Speed Retrieval Based on Deep Learning".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022):13. |
入库方式： OAI收割
来源：海洋研究所
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