A Dual-Source Deep Learning Model for Mapping Wet Anabranches in Braided Rivers on the Tibetan Plateau
文献类型：期刊论文
| 作者 | Liu, Xiaolu1,2; Ma, Xiaoyi1; Qin, Shuai3; Liu, Tang1; Zhou, Chenghu1 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 18页码:12773-12785 |
| 关键词 | Rivers Clouds Feature extraction Monitoring Sentinel-1 Remote sensing Data models Radar Floods Spatial resolution Braided rivers cloud-robust classification deep learning remote sensing |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2025.3568272 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | The Tibetan Plateau is home to dozens of large braided rivers, a phenomenon that is extremely rare in the global distribution of alluvial rivers. Dynamically monitoring these rivers is crucial for understanding the unique sedimentary processes and hydrodynamic patterns of the plateau. However, the complex network of water channels and sandbars, coupled with frequent cloud cover, intricate terrain, and numerous branches in the plateau region, poses significant challenges to traditional monitoring and extraction methods. To address these challenges, this study proposes a novel dual-feature encoder deep learning model, DIResU-Net, which integrates Sentinel-1 and Sentinel-2 data to achieve high-precision and long-term extraction of braided rivers. The model employs dual encoders to extract features from optical and radar data, combined with a unified decoder and an attention mechanism for efficient feature fusion. Additionally, a multicomposite loss function was designed to enhance the model's performance. Experimental results demonstrate that the proposed DIResU-Net achieves a high F1-score of 0.87 and IoU of 0.79 under cloud-free conditions, significantly outperforming traditional single-source models. In cloud-covered scenarios, the model maintains robust performance (IoU > 0.73) by leveraging the complementary advantages of Sentinel-1 and Sentinel-2 data. The model also exhibits strong temporal generalization in mapping river morphology from 2019 to 2024, highlighting its value for long-term monitoring and environmental management. Further analysis of morphological parameters-such as river width, channel density, and braiding index-reveals clear seasonal and interannual fluctuations across typical river sections, reflecting the dynamic nature of braided river systems on the plateau. This study provides a scalable framework for high-resolution mapping and long-term monitoring of braided rivers, with implications for hydrological analysis and basin-scale management on the Tibetan Plateau. |
| URL标识 | 查看原文 |
| WOS关键词 | WATER ; INDEX |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001499642600002 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/214663]  |
| 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, Tang |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 101408, Peoples R China; 3.Hebei South Canal River Affairs Ctr, Hangzhou 061001, Hebei, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Xiaolu,Ma, Xiaoyi,Qin, Shuai,et al. A Dual-Source Deep Learning Model for Mapping Wet Anabranches in Braided Rivers on the Tibetan Plateau[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2025,18:12773-12785. |
| APA | Liu, Xiaolu,Ma, Xiaoyi,Qin, Shuai,Liu, Tang,&Zhou, Chenghu.(2025).A Dual-Source Deep Learning Model for Mapping Wet Anabranches in Braided Rivers on the Tibetan Plateau.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,18,12773-12785. |
| MLA | Liu, Xiaolu,et al."A Dual-Source Deep Learning Model for Mapping Wet Anabranches in Braided Rivers on the Tibetan Plateau".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 18(2025):12773-12785. |
入库方式： OAI收割
来源：地理科学与资源研究所
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。