The Fusion of Physical, Textural, and Morphological Information in SAR Imagery for Hurricane Wind Speed Retrieval Based on Deep Learning

doi:10.1109/TGRS.2022.3174824

IOCAS-IR > 海洋环流与波动重点实验室

	The Fusion of Physical, Textural, and Morphological Information in SAR Imagery for Hurricane Wind Speed Retrieval Based on Deep Learning
	Mu, Shanshan1,2 ; Li, Xiaofeng 1,3; Wang, Haoyu 1,2
	2022
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	60 页码:13
通讯作者	Li, Xiaofeng([email protected])
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.
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
收录类别	SCI
语种	英语
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
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000804647900031
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：16[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/179483
专题	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, Inst Oceanog, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 2.Univ Chinese Acad Sci, Sch Oceanog, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China
第一作者单位	中国科学院海洋研究所
通讯作者单位	中国科学院海洋研究所; 中国科学院海洋大科学研究中心
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.

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
_Applet_2022-05-15_d（2103KB）	期刊论文	出版稿	限制开放	CC BY-NC-SA	浏览