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

doi:10.1109/TGRS.2022.3197149

CORC > 海洋研究所 > 中国科学院海洋研究所 > 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 7; Liu, Bin 5,6,7; Zheng, Gang 3,4,6; Li, Xiaofeng 1,2
刊名	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
	2022
卷号	60 页码:18
关键词	Deep convolutional neural networks (DCNNs) deep learning environment monitoring intertidal zone MB-U-2-ACNet synthetic aperture radar (SAR) imagery
ISSN号	0196-2892
DOI	10.1109/TGRS.2022.3197149
通讯作者	Liu, Bin(bliu@shou.edu.cn) ; Li, Xiaofeng(xiaofeng.li@ieee.org)
英文摘要	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.
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
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000844159700004
内容类型	期刊论文
源URL	[http://ir.qdio.ac.cn/handle/337002/180789]
专题	海洋研究所_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.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 3.Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai 519082, Peoples R China 4.Zhejiang Univ, Ocean Coll, Zhoushan 316021, Peoples R China 5.Minist Nat Resources, Key Lab Marine Ecol Monitoring & Restorat Technol, Shanghai 200137, Peoples R China 6.Minist Nat Resources, State Key Lab Satellite Ocean Environm Dynam, Inst Oceanog 2, Hangzhou 310012, Peoples R China 7.Shanghai Ocean Univ, Coll Marine Sci, Shanghai 201306, Peoples R China
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.