Coastal Zone Extraction Algorithm Based on Multilayer Depth Features for Hyperspectral Images
文献类型：期刊论文
| 作者 | Qiu, Shi4; Ye, Huping2,3; Liao, Xiaohan1,2,3 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2023 |
| 卷号 | 61页码:15 |
| 关键词 | Feature extraction Hyperspectral imaging Sea measurements Convolutional neural networks Three-dimensional displays Remote sensing Water quality 3-D convolutional neural network (CNN) depth characteristic hyperspectrum multilevel remote sensing squeeze and excitation network (SENet) |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2023.3321478 |
| 通讯作者 | Ye, Huping(yehp@igsnrr.ac.cn) |
| 英文摘要 | The coastal zone is the most active natural area on the Earth's surface and has the most favorable resources and environmental conditions. Therefore, it is of great significance to conduct research based on the coastal zone. Hyperspectral remote sensing images have spatial and spectral dimensions that reflect the spatial distribution and can analyze the compositional information, which has been widely used for feature analysis and observation of ground objects. In this article, we propose a coastal zone extraction algorithm based on multilayer depth features for hyperspectral images (HSIs). The main contributions are as follows: 1) the Huanjing satellite hyperspectral coastal zone database is built for the first time, image composition is analyzed, and the noise removal algorithm is yielded; 2) 3-D attention networks that are capable of carrying spatial and interspectral information are proposed; and 3) A 3-D convolutional neural network (CNN) with squeeze and excitation network (SENet) tandem structure is proposed to fully exploit detailed information, and a multilayer feature extraction framework is built. We analyze four typical coastal zone patterns, and the experimental results show that our proposed algorithm can achieve coastal zone extraction with an average Kappa coefficient of 0.92, which is 0.06 higher than the mainstream algorithms. Our algorithm also shows good performance in complex environments. It provides a basis for further research on coastal zones. |
| WOS关键词 | WATER-QUALITY ; SEGMENTATION |
| 资助项目 | Strategic Priority Research Program of Chinese Academy of Sciences[XDA2003030201] ; Light of West China[XAB2022YN10] ; National Natural Science Foundation of China[41971359] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001087763100039 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | Strategic Priority Research Program of Chinese Academy of Sciences ; Light of West China ; National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/199045]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Ye, Huping |
| 作者单位 | 1.Chinese Acad Sci, Res Ctr UAV Applicat & Regulat, Beijing 100101, Peoples R China 2.Civil Aviat Adm China, Key Lab Low Altitude Geog Informat & Air Route, Beijing 100101, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol, Xian 710119, Peoples R China |
| 推荐引用方式 GB/T 7714 | Qiu, Shi,Ye, Huping,Liao, Xiaohan. Coastal Zone Extraction Algorithm Based on Multilayer Depth Features for Hyperspectral Images[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2023,61:15. |
| APA | Qiu, Shi,Ye, Huping,&Liao, Xiaohan.(2023).Coastal Zone Extraction Algorithm Based on Multilayer Depth Features for Hyperspectral Images.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,61,15. |
| MLA | Qiu, Shi,et al."Coastal Zone Extraction Algorithm Based on Multilayer Depth Features for Hyperspectral Images".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 61(2023):15. |
入库方式： OAI收割
来源：地理科学与资源研究所
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