Populus Euphratica extraction based on deep learning of spatiotemporal Information using Sentinel data
文献类型：期刊论文
| 作者 | Li, Hao4; Zhao, Qinyu3; Hu, Jiacong4; Zou, Jiawei2; Ding, Chao3; Ji, Luyan1,2; Liu, Suhong3; Cheng, Weiming5 |
| 刊名 | INTERNATIONAL JOURNAL OF REMOTE SENSING
 |
| 出版日期 | 2025-07-26 |
| 卷号 | N/A |
| 关键词 | Populus euphratica mapping deep learning Spatiotemporal Sentinel |
| ISSN号 | 0143-1161 |
| DOI | 10.1080/01431161.2025.2535747 |
| 产权排序 | 5 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | Populus Euphratica has always been one of the most important vegetation types in desert regions, playing a crucial role in maintaining the ecological balance of the desert ecosystem. With the development of remote sensing technology, large-scale monitoring of Populus Euphratica forests through remote sensing has become feasible. However, the extraction of Populus Euphratica forests faces two key challenges: Spectral Confusion and Complex Spatial Distribution. Although existing extraction methods consider temporal features, they have not fully utilized the spatiotemporal information. To address this, we propose a spatiotemporal-based multi-source remote sensing method for Populus Euphratica forest extraction, named STP-Net (Spatiotemporal Populus Euphratica Extraction Network). This method fully integrates the spatiotemporal information of multi-source remote sensing satellite images to accurately extract Populus Euphratica forests. Furthermore, to validate and assess the performance of the algorithm, we created a dataset for the Populus Euphratica forest extraction task based on multi-source spatiotemporal satellite imagery. The experimental results demonstrate that our algorithm outperforms current state-of-the-art methods in the task of extracting Populus Euphratica forests, and it also offers a fast mapping speed, making it suitable for large-scale mapping of Populus Euphratica forests. |
| URL标识 | 查看原文 |
| WOS关键词 | TARIM RIVER ; TIME-SERIES ; FORESTS |
| WOS研究方向 | Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001536149800001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/215530]  |
| 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 | Hu, Jiacong |
| 作者单位 | 1.Chinese Acad Sci, Key Lab Technol Geospatial Informat Proc & Applica, Beijing, Peoples R China; 2.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing, Peoples R China; 3.Beijing Normal Univ, Fac Arts & Sci, Dept Geog Sci, Zhuhai, Peoples R China; 4.Beijing Normal Univ, Expt Teaching Platform, Zhuhai 519087, Peoples R China; 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Hao,Zhao, Qinyu,Hu, Jiacong,et al. Populus Euphratica extraction based on deep learning of spatiotemporal Information using Sentinel data[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2025,N/A. |
| APA | Li, Hao.,Zhao, Qinyu.,Hu, Jiacong.,Zou, Jiawei.,Ding, Chao.,...&Cheng, Weiming.(2025).Populus Euphratica extraction based on deep learning of spatiotemporal Information using Sentinel data.INTERNATIONAL JOURNAL OF REMOTE SENSING,N/A. |
| MLA | Li, Hao,et al."Populus Euphratica extraction based on deep learning of spatiotemporal Information using Sentinel data".INTERNATIONAL JOURNAL OF REMOTE SENSING N/A(2025). |
入库方式： OAI收割
来源：地理科学与资源研究所
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