Development of a Dual-Attention U-Net Model for Sea Ice and Open Water Classification on SAR Images

doi:10.1109/LGRS.2021.3058049

IOCAS-IR > 海洋环流与波动重点实验室

	Development of a Dual-Attention U-Net Model for Sea Ice and Open Water Classification on SAR Images
	Ren, Yibin 1,2; Li, Xiaofeng 1,2; Yang, Xiaofeng 3,4; Xu, Huan 5
	2022
发表期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
ISSN	1545-598X
卷号	19 页码:5
通讯作者	Li, Xiaofeng([email protected])
摘要	This study develops a deep learning (DL) model to classify the sea ice and open water from synthetic aperture radar (SAR) images. We use the U-Net, a well-known fully convolutional network (FCN) for pixel-level segmentation, as the model backbone. We employ a DL-based feature extracting model, ResNet-34, as the encoder of the U-Net. To achieve high accuracy classifications, we integrate the dual-attention mechanism into the original U-Net to improve the feature representations, forming a dual-attention U-Net model (DAU-Net). The SAR images are obtained from Sentinel-1A. The dual-polarized information and the incident angle of SAR images are model inputs. We used 15 dual-polarized images acquired near the Bering Sea to train the model and employ the other three images to test the model. Experiments show that the DAU-Net could achieve pixel-level classification; the dual-attention mechanism can improve the classification accuracy. Compared with the original U-Net, DAU-Net improves the intersection over union (IoU) by 7.48.% points, 0.96.% points, and 0.83.% points on three test images. Compared with the recently published model DenseNetFCN, the three improvement IoU values of DAU-Net are 3.04.% points, 2.53.% points, and 2.26.% points, respectively.
关键词	Sea ice Radar polarimetry Feature extraction Decoding Oceans Kernel Image segmentation Dual-attention sea ice and open water classification synthetic aperture radar (SAR) image U-Net
DOI	10.1109/LGRS.2021.3058049
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]; China Postdoctoral Science Foundation[2019M662452]; Key Research and Development Project of Shandong Province[2019JZZY010102]; Key Deployment Project of Center for Ocean Mega-Science, CAS[COMS2019R02]; CAS Program[Y9KY04101L]; National Natural Science Foundation of China[41776183]
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:000732885500001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：83[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/337002/177570
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 Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 2.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China 3.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China 4.Key Lab Earth Observat Hainan Prov, Sanya 572029, Peoples R China 5.Jiangsu Ocean Univ, Sch Geomat & Marine Informat, Lianyungang 222005, Peoples R China
第一作者单位	海洋环流与波动重点实验室; 中国科学院海洋大科学研究中心
通讯作者单位	海洋环流与波动重点实验室; 中国科学院海洋大科学研究中心
推荐引用方式 GB/T 7714	Ren, Yibin,Li, Xiaofeng,Yang, Xiaofeng,et al. Development of a Dual-Attention U-Net Model for Sea Ice and Open Water Classification on SAR Images[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2022,19:5.
APA	Ren, Yibin,Li, Xiaofeng,Yang, Xiaofeng,&Xu, Huan.(2022).Development of a Dual-Attention U-Net Model for Sea Ice and Open Water Classification on SAR Images.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,19,5.
MLA	Ren, Yibin,et al."Development of a Dual-Attention U-Net Model for Sea Ice and Open Water Classification on SAR Images".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19(2022):5.

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