Fine-Scale Small Water Body Uncovered by GF-2 Remote Sensing and Multifeature Deep Learning Model
文献类型：期刊论文
| 作者 | Jiang, Yixin3; Wang, Chunlin2; Huang, Zhaji2; Li, Dandan1; Wang, Biao3; Wu, Yanlan5; Liu, Hui3; Liu, Zihan4,5 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 18页码:16750-16768 |
| 关键词 | Water resources Urban areas Remote sensing Spatial resolution Feature extraction Deep learning Rivers Land surface Ecosystems Biological system modeling Multifeature integration small water body extraction spatial distribution urban water bodies |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2025.3583918 |
| 产权排序 | 5 |
| 文献子类 | Article |
| 英文摘要 | Small water bodies are essential for hydrological connectivity, efficient use of water resources, and ecological conservation. Existing water body extraction models face issues, such as detail loss, misclassification, and incomplete coverage. The lack of fine-scale remote sensing imagery and well-performing models has hindered the ability to track small water body dynamics, especially in heterogeneous urban areas. To address this, the multifeature joint perception convolutional network (MSFCN) is proposed for the fine-scale extraction of small water bodies using GF-2 satellite data. Spatial characteristics of small water bodies in different urban zones and their relationship with overall urban water resources are then analyzed. Results show that MSFCN performs well with an overall accuracy of 0.91 in extracting small water bodies. Spatial analysis indicates that small water bodies are mainly found in suburban areas (80%), followed by the expansion zone (14%) and the city core (6%). In the city core, small water bodies are predominantly artificial, often linked to construction activities, while in suburban areas, they are mainly agricultural, serving irrigation purposes. The analysis also reveals a negative correlation between small and large water bodies in all urban areas, suggesting the ecological sensitivity of these water bodies and their crucial role in maintaining hydrological balance. These findings provide important technical support for large-scale extraction of small water bodies and offer valuable insights for urban planning and ecological management. |
| URL标识 | 查看原文 |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001531874800012 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/215341]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Liu, Zihan |
| 作者单位 | 1.GuangDong Ecol & Environm Monitoring Ctr, Guangzhou Sub Branch, Guangzhou 510000, Peoples R China; 2.Anhui Prov Inst Water Resources Sci, Hefei 230601, Peoples R China; 3.Anhui Univ, Sch Resources & Environm Engn, Hefei 230601, Peoples R China; 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 5.Anhui Univ, Sch Artificial Intelligence, Hefei 230601, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Jiang, Yixin,Wang, Chunlin,Huang, Zhaji,et al. Fine-Scale Small Water Body Uncovered by GF-2 Remote Sensing and Multifeature Deep Learning Model[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2025,18:16750-16768. |
| APA | Jiang, Yixin.,Wang, Chunlin.,Huang, Zhaji.,Li, Dandan.,Wang, Biao.,...&Liu, Zihan.(2025).Fine-Scale Small Water Body Uncovered by GF-2 Remote Sensing and Multifeature Deep Learning Model.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,18,16750-16768. |
| MLA | Jiang, Yixin,et al."Fine-Scale Small Water Body Uncovered by GF-2 Remote Sensing and Multifeature Deep Learning Model".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 18(2025):16750-16768. |
入库方式： OAI收割
来源：地理科学与资源研究所
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