Institutional Repository of Key Laboratory of Ocean Circulation and Wave Studies, Institute of Oceanology, Chinese Academy of Sciences
| Automatic Waterline Extraction and Topographic Mapping of Tidal Flats From SAR Images Based on Deep Learning | |
| Zhang, Shuangshang1,2; Xu, Qing3; Wang, Haoyu1,2; Kang, Yanyan4; Li, Xiaofeng1,2 | |
| 2022-01-28 | |
| 发表期刊 | GEOPHYSICAL RESEARCH LETTERS
 |
| ISSN | 0094-8276 |
| 卷号 | 49期号:2页码:13 |
| 通讯作者 | Li, Xiaofeng([email protected]) |
| 摘要 | This study presented an intuitive approach to derive large-scale tidal flat's Digital Elevation Model (DEM). We first developed an automated method for accurately extracting the waterline from Synthetic Aperture Radar images acquired in Subei Sandbanks along the Yellow Sea coast of China between 2015 and 2020 based on deep convolutional neural networks. The statistical results show this method has appreciable accuracy for efficient waterline extraction even under complex imaging conditions with a mean recall and precision of 0.90 and 0.80, respectively. Then the pixel-level extracted waterlines are calibrated with a global tide model to construct the large-scale tidal flat's DEM in the study region. The comparison against in situ topographic data shows an error of 29 cm, demonstrating the usefulness of monitoring the morpho-sedimentary evolution in intertidal areas. Furthermore, the Subei Sandbanks remained stable from 2015 to 2020, while the coastal region changed drastically due to human activities. |
| 关键词 | synthetic aperture radar tidal flat deep learning waterline DEM |
| DOI | 10.1029/2021GL096007 |
| 收录类别 | SCI |
| 语种 | 英语 |
| 资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB42040401]; Key R&D project of Shandong Province[2019JZZY010102]; National Natural Science Foundation of China-Shandong Science Foundation[U2006211]; Key deployment project of Center for Ocean Mega-Science, CAS[COMS2019R02]; CAS[Y9KY04101 L]; National Natural Science Foundation of China[41976163] |
| WOS研究方向 | Geology |
| WOS类目 | Geosciences, Multidisciplinary |
| WOS记录号 | WOS:000751642800047 |
| 出版者 | AMER GEOPHYSICAL UNION |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.qdio.ac.cn/handle/337002/177991 |
| 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, Peoples R China 2.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao, Peoples R China 3.Ocean Univ China, Coll Marine Technol, Fac Informat Sci & Engn, Qingdao, Peoples R China 4.Hohai Univ, Coll Oceanog, Nanjing, 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 | Zhang, Shuangshang,Xu, Qing,Wang, Haoyu,et al. Automatic Waterline Extraction and Topographic Mapping of Tidal Flats From SAR Images Based on Deep Learning[J]. GEOPHYSICAL RESEARCH LETTERS,2022,49(2):13. |
| APA | Zhang, Shuangshang,Xu, Qing,Wang, Haoyu,Kang, Yanyan,&Li, Xiaofeng.(2022).Automatic Waterline Extraction and Topographic Mapping of Tidal Flats From SAR Images Based on Deep Learning.GEOPHYSICAL RESEARCH LETTERS,49(2),13. |
| MLA | Zhang, Shuangshang,et al."Automatic Waterline Extraction and Topographic Mapping of Tidal Flats From SAR Images Based on Deep Learning".GEOPHYSICAL RESEARCH LETTERS 49.2(2022):13. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Geophysical Research（5243KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 浏览 | |
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。
修改评论