Deep learning techniques for enhanced sea-ice types classification in the Beaufort Sea via SAR imagery

doi:10.1016/j.rse.2024.114204

IOCAS-IR > 海洋环流与波动重点实验室

	Deep learning techniques for enhanced sea-ice types classification in the Beaufort Sea via SAR imagery
	Huang, Yan; Ren, Yibin; Li, Xiaofeng
	2024-07-01
发表期刊	REMOTE SENSING OF ENVIRONMENT
ISSN	0034-4257
卷号	308 页码:19
通讯作者	Ren, Yibin(yibinren@qdio.ac.cn)
摘要	This study proposes a dual-branch encoder U-Net (DBU-Net) deep learning model to classify sea ice types based on synthetic aperture radar (SAR) images in the Beaufort Sea. The DBU-Net can segment multi-year ice (MYI), first-year ice (FYI), open water (OW), and leads on SAR images. We design a dual-branch encoder to fuse the polarization and the grey-level co-occurrence matrix (GLCM) information of SAR images to improve the model's classification capability. The model is subsequently fine-tuned using lead samples to identify leads. 24 Sentinel-1 SAR images acquired in the Beaufort Sea are utilized for model training and testing. The accuracy (Acc), mean intersection over union (mIoU), and kappa coefficient (Kappa) are employed as evaluation metrics. Experiments show that DBU-Net achieves 91.83%/0.841/0.849 in Acc/mIoU/Kappa in classifying MYI, FYI, and OW, significantly outperforming three traditional models based on support vector machine, random forest, or convolutional neural network. Compared with the original U-Net, the dual-branch encoder and the GLCMs improve 1.45%/4.4%/2.8% in Acc/mIoU/Kappa in MYI, FYI, and OW. Acc/mIoU/Kappa metrics of leads detection is 99.49%/0.801/0.754. Besides, 454 Sentinel-1 SAR images are fed into the optimal DBU-Net to generate 80 m sea ice products in the Beaufort Sea for winters 2018-2022. As the MYI draws wide attention and the FYI and MYI are complementary in the area during the Winter, we discuss the variation of MYI based on the generated sea ice products and explore the relationship between MYI's variation and the Beaufort High. We found that the MYI export in the 2018/19 winter was due to large summer sea ice remains and the abnormal sea ice motion caused by the southeast shifting Beaufort Atmospheric Pressure High (Beaufort High). The MYI import in the 2020/21 winter was due to a strong northward MYI import caused by the powerful Beaufort High.
关键词	Synthetic aperture radar (SAR) Sea ice classification Deep learning Beaufort sea
DOI	10.1016/j.rse.2024.114204
收录类别	SCI
语种	英语
资助项目	Laoshan Laboratory Science and Technology Innovation Project[LSKJ202202302] ; Laoshan Laboratory Science and Technology Innovation Project[LSKJ202204302] ; National Natural Science Foundation of China[42206202] ; National Natural Science Foundation of China[42221005] ; China-Portugal Xinghai Belt and Road Joint laboratory and joint research on new air and sea technologies[2022YFE0204600]
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001241656200001
出版者	ELSEVIER SCIENCE INC
WOS关键词	SYNTHETIC-APERTURE RADAR ; PASSIVE MICROWAVE ; TEXTURE ANALYSIS ; SYSTEM ; LEADS
引用统计	被引频次：2[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/337002/186000
专题	海洋环流与波动重点实验室
通讯作者	Ren, Yibin
作者单位	Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Observat & Forecasting, Key Lab Ocean Circulat & Waves, Qingdao, 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
推荐引用方式 GB/T 7714	Huang, Yan,Ren, Yibin,Li, Xiaofeng. Deep learning techniques for enhanced sea-ice types classification in the Beaufort Sea via SAR imagery[J]. REMOTE SENSING OF ENVIRONMENT,2024,308:19.
APA	Huang, Yan,Ren, Yibin,&Li, Xiaofeng.(2024).Deep learning techniques for enhanced sea-ice types classification in the Beaufort Sea via SAR imagery.REMOTE SENSING OF ENVIRONMENT,308,19.
MLA	Huang, Yan,et al."Deep learning techniques for enhanced sea-ice types classification in the Beaufort Sea via SAR imagery".REMOTE SENSING OF ENVIRONMENT 308(2024):19.