A comparative analysis of machine learning-based methods for impervious surface mapping using SAR and optical data
文献类型：期刊论文
| 作者 | He, Siqi2; Zhu, Lihong1; Li, Yiman5; Xia, Qing1; Zheng, Qiong1; Wang, Zheng4; Zou, Xinyu3 |
| 刊名 | GEOCARTO INTERNATIONAL
 |
| 出版日期 | 2025-12-31 |
| 卷号 | 40期号:1页码:2521833 |
| 关键词 | Impervious surface random forest XGBoost Sentinel-1/2 feature selection |
| ISSN号 | 1010-6049 |
| DOI | 10.1080/10106049.2025.2521833 |
| 产权排序 | 5 |
| 文献子类 | Article |
| 英文摘要 | Accurate and timely access to information about impervious layers is essential for urban development and ecological environment. This study employs the random forest (RF) and extreme gradient boosting algorithm to rank the significance of features, which include sentinel-1 polarization and sentinel-2 spectral information, texture features, and vegetation indices, and analyze the contribution of each feature using the SHAP method. The change analysis of the impervious layer in Changsha County from 2016 to 2024 was conducted based on the better machine learning algorithm and indicators for extracting the impervious layer. The outperformed RF algorithm had an overall classification accuracy of over 92% from 2016 to 2024. Interestingly, there was a notable rise of the area of impervious surfaces in 2019 and a substantial fall in 2023, whereas the other years saw slight changes. The suggested approach can serve as a helpful guide for extracting the impervious layer chosen via index optimization. |
| URL标识 | 查看原文 |
| WOS关键词 | RANDOM FOREST ; LANDSAT ; AREA ; DYNAMICS |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001521200400001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/215302]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 通讯作者 | Zhu, Lihong |
| 作者单位 | 1.Changsha Univ Sci & Technol, Sch Aeronaut Engn, Changsha, Peoples R China; 2.Changsha Univ Sci & Technol, Sch Transportat, Dept Surveying Engn, Changsha, Peoples R China; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing, Peoples R China 4.Chengdu Planning Res & Applicat Technol Ctr, Chengdu, Sichuan, Peoples R China; 5.China Univ Geosci, Sch Informat Engn, Beijing, Peoples R China; |
| 推荐引用方式 GB/T 7714 | He, Siqi,Zhu, Lihong,Li, Yiman,et al. A comparative analysis of machine learning-based methods for impervious surface mapping using SAR and optical data[J]. GEOCARTO INTERNATIONAL,2025,40(1):2521833. |
| APA | He, Siqi.,Zhu, Lihong.,Li, Yiman.,Xia, Qing.,Zheng, Qiong.,...&Zou, Xinyu.(2025).A comparative analysis of machine learning-based methods for impervious surface mapping using SAR and optical data.GEOCARTO INTERNATIONAL,40(1),2521833. |
| MLA | He, Siqi,et al."A comparative analysis of machine learning-based methods for impervious surface mapping using SAR and optical data".GEOCARTO INTERNATIONAL 40.1(2025):2521833. |
入库方式： OAI收割
来源：地理科学与资源研究所
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