A Deep Learning Model for Green Algae Detection on SAR Images

doi:10.1109/TGRS.2022.3215895

IOCAS-IR > 海洋环流与波动重点实验室

	A Deep Learning Model for Green Algae Detection on SAR Images
	Guo, Yuan 1,2,3; Gao, Le 1,2; Li, Xiaofeng 1,2
	2022
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	60 页码:14
通讯作者	Li, Xiaofeng([email protected])
摘要	This study developed a textural-enhanced deep learning (DL) model based on the classic U-net framework for green algae detection in Sentinel-1 synthetic aperture radar (SAR) imagery. Four special modifications are made in the framework: texture-fused input dataset, texture concatenation to effectively use the texture information, weighted loss function to settle the imbalance of algae-seawater samples, and an attention module to facilitate model focus on the discriminative features efficiently. To build the proposed model, we collected 119 Sentinel-1 SAR images acquired in the Yellow Sea and manually labeled 8441 samples, among which 4421/1896/2124 were used as the training/validation/testing dataset, respectively. Experiments show that the classification achieves the mean intersection over union (mIOU) of 86.31%, outperforming previous DL methods. Furthermore, each modification is effective, and the weighted loss function plays the most critical role. Moreover, we monitored green tide in the Yellow Sea from 2019 to 2021 using the proposed model and analyzed the relationship between green tide interannual variation and two primary environmental factors: nitrate concentration and sea surface temperature (SST). The interannual variation is characterized via three crucial indexes: bloom duration, coverage area, and nearshore damage. The detection results reveal that the bloom duration is the longest (shortest) in 2019 (2020), corresponding to the biggest (smallest) coverage area in 2019 (2020). In addition, the nearshore damage is the heaviest (lightest) in 2021 (2020). We also found that the interannual variation of green tide scales is partly related to the available nitrate concentration and SST variation in algae-distributed regions.
关键词	Green products Algae Radar polarimetry Tides Ocean temperature Feature extraction Sea surface Deep learning (DL) green algae Sentinel-1 synthetic aperture radar (SAR) image Yellow Sea
DOI	10.1109/TGRS.2022.3215895
收录类别	SCI
语种	英语
资助项目	Major Project of the 14th Five-Year Plan of Pilot National Laboratory for Marine Science and Technology (Qingdao)[2022QNLM050301-2]; National Natural Science Foundation of China-Shandong Province[U2006211]; Major Projects of the National Natural Science Foundation of China[42090044]; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDA19060101]; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB42000000]; Major Scientific and Technological Innovation Projects in Shandong Province[2019JZZY010102]; Major Scientific and Technological Innovation Projects in Shandong Province[Y9KY04101L]; Major Scientific and Technological Innovation Projects in Shandong Province[COMS2019R02]
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:000882005800007
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：12[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/337002/180323
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.Univ Chinese Acad Sci, Coll Marine Sci, Beijing 100049, 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	Guo, Yuan,Gao, Le,Li, Xiaofeng. A Deep Learning Model for Green Algae Detection on SAR Images[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60:14.
APA	Guo, Yuan,Gao, Le,&Li, Xiaofeng.(2022).A Deep Learning Model for Green Algae Detection on SAR Images.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60,14.
MLA	Guo, Yuan,et al."A Deep Learning Model for Green Algae Detection on SAR Images".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022):14.