Environment Monitoring of Shanghai Nanhui Intertidal Zone With Dual-Polarimetric SAR Data Based on Deep Learning

doi:10.1109/TGRS.2022.3197149

IOCAS-IR > 海洋环流与波动重点实验室

	Environment Monitoring of Shanghai Nanhui Intertidal Zone With Dual-Polarimetric SAR Data Based on Deep Learning
	Liu, Guangyang 1; Liu, Bin 1,2,3; Zheng, Gang 2,4,5; Li, Xiaofeng 6,7
	2022
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	60 页码:18
通讯作者	Liu, Bin(bliu@shou.edu.cn) ; Li, Xiaofeng(xiaofeng.li@ieee.org)
摘要	Satellite-based synthetic aperture radar (SAR) can provide low-cost, frequent environment monitoring for dynamic intertidal zones. The critical problem is to realize pixel-level classification of SAR images of the intertidal zones with excellent and robust performance. Recently, deep learning, in particular deep convolutional neural networks, has provided us with promising solutions to this problem. Based on a sophisticated deep learning-based pixel-level classification model U-2-Net, we propose an MB-U-2-ACNet model suitable for intertidal zone land cover classification using dual-polarimetric SAR data integrated with environmental information, such as wind speed and tide level information. The MB-U-2-ACNet model has a multibranch nested U-shaped encoding-decoding structure. We extract and fuse features from multiple data sources, including satellite remote sensing and environmental information, by establishing the multibranch structure. Furthermore, we propose an asymmetric convolution residual U-block for each encoding-decoding stage to improve the model's feature extraction ability. Moreover, the model with attention mechanisms better distinguishes the importance of features from the channel's perspectives and spatial dimensions. We construct a dataset with 106 Sentinel-1 SAR images from 2016 to 2020 for environmental monitoring in the intertidal zone of Shanghai Nanhui. On the dataset, the proposed model reaches the overall classification accuracy of 96.40% and the mean intersection over union score of 0.8307. The experiments show the advantages of the proposed model compared with the benchmarking models due to better feature extraction and multisource information fusion. In addition, the contributions of every added substructure are analyzed systematically.
关键词	Deep convolutional neural networks (DCNNs) deep learning environment monitoring intertidal zone MB-U-2-ACNet synthetic aperture radar (SAR) imagery
DOI	10.1109/TGRS.2022.3197149
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[42006159] ; Open Fund of the State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources[QNHX2020] ; Open Fund of the State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources[QNHX2238] ; Key Research and Development Project of Shandong Province[2019JZZY010102] ; Key Deployment Project of Center for Ocean Mega-Science, Chinese Academy of Sciences (CAS)[COMS2019R02] ; CAS Program[Y9KY04101L] ; Zhejiang Provincial Natural Science Foundation of China[LR21D060002]
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:000844159700004
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：10[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/337002/180790
专题	海洋环流与波动重点实验室
通讯作者	Liu, Bin; Li, Xiaofeng
作者单位	1.Shanghai Ocean Univ, Coll Marine Sci, Shanghai 201306, Peoples R China 2.Minist Nat Resources, State Key Lab Satellite Ocean Environm Dynam, Inst Oceanog 2, Hangzhou 310012, Peoples R China 3.Minist Nat Resources, Key Lab Marine Ecol Monitoring & Restorat Technol, Shanghai 200137, Peoples R China 4.Zhejiang Univ, Ocean Coll, Zhoushan 316021, Peoples R China 5.Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China 6.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 7.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China
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	Liu, Guangyang,Liu, Bin,Zheng, Gang,et al. Environment Monitoring of Shanghai Nanhui Intertidal Zone With Dual-Polarimetric SAR Data Based on Deep Learning[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60:18.
APA	Liu, Guangyang,Liu, Bin,Zheng, Gang,&Li, Xiaofeng.(2022).Environment Monitoring of Shanghai Nanhui Intertidal Zone With Dual-Polarimetric SAR Data Based on Deep Learning.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60,18.
MLA	Liu, Guangyang,et al."Environment Monitoring of Shanghai Nanhui Intertidal Zone With Dual-Polarimetric SAR Data Based on Deep Learning".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022):18.