A Deep Learning Model for Green Algae Detection on SAR Images
文献类型：期刊论文
| 作者 | Guo, Yuan1,2,3; Gao, Le1,2; Li, Xiaofeng1,2 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2022 |
| 卷号 | 60页码:14 |
| 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 |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2022.3215895 |
| 通讯作者 | Li, Xiaofeng(xiaofeng.li@ieee.org) |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000882005800007 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.qdio.ac.cn/handle/337002/180322]  |
| 专题 | 海洋研究所_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 |
| 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. |
入库方式： OAI收割
来源：海洋研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。