Identification of tea plantations in typical plateau areas with the combination of Sentinel-1/2 optical and radar remote sensing data based on feature selection algorithm
文献类型：期刊论文
| 作者 | Gao, Shanchuan; Tang, Bo-Hui; Huang, Liang; Chen, Guokun5 |
| 刊名 | INTERNATIONAL JOURNAL OF REMOTE SENSING
 |
| 出版日期 | 2023-04-14 |
| 卷号 | N/A |
| 关键词 | sentinel-1 2 tea plantations plateau areas random forest machine learning recursive feature elimination |
| DOI | 10.1080/01431161.2023.2198655 |
| 文献子类 | Article ; Early Access |
| 英文摘要 | Efficiently and accurately identifying the spatial distribution of tea plantations in the subtropical plateau regions of southwest China is of great significance for ecological and environmental protection. However, the lands of those regions are fragmented with complex vegetation types. Moreover, there is much cloudy and rainy weather over those areas, making it very difficult to identify tea plantations using only optical remote sensing data. In order to solve these problems, this paper uses Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data and Sentinel-2 (S2) optical data to design seven classification feature combinations to explore the influence of red edge features, radar features and texture features on the identification accuracy of tea plantations. The feasibility of Jeffreys-Matusita distance (JM) feature selection and Recursive Feature Elimination (RFE) feature selection algorithm to find the optimal feature combination is verified, and the distribution of tea plantations in the study area is acquired by using the object-oriented random forest algorithm. The study shows that (1) the combination of SAR data and optical data can effectively improve the identification accuracy of tea plantations. (2) S2 red edge features and S1 radar features can significantly improve the accuracy of the identification results of tea plantations. (3) After applying the JM distance and RFE feature selection algorithms, the producer's accuracy of tea plantations is improved by 1.39% and 2.38%, and the user's accuracy is improved by 1.02% and 1.3%, respectively, compared with the identification of all features. The overall accuracy of the random forest algorithm combined with RFE is 93.43%. This study proposes the application of feature selection algorithms in identification of tea plantations, which improves accuracy and increases efficiency while minimizing redundant features and provides an effective approach to identify tea plantations in cloudy and rainy areas in the subtropical plateau of southern China. |
| WOS关键词 | TEXTURES ; IMAGERY ; MAIZE |
| WOS研究方向 | Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:000971627100001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200787]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Tang, Bo-Hui 2.[Gao, Shanchuan 3.Kunming Univ Sci & Technol, Fac Land Resource Engn, Kunming 650031, Yunnan, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 5.Huang, Liang |
| 推荐引用方式 GB/T 7714 | Gao, Shanchuan,Tang, Bo-Hui,Huang, Liang,et al. Identification of tea plantations in typical plateau areas with the combination of Sentinel-1/2 optical and radar remote sensing data based on feature selection algorithm[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING,2023,N/A. |
| APA | Gao, Shanchuan,Tang, Bo-Hui,Huang, Liang,&Chen, Guokun.(2023).Identification of tea plantations in typical plateau areas with the combination of Sentinel-1/2 optical and radar remote sensing data based on feature selection algorithm.INTERNATIONAL JOURNAL OF REMOTE SENSING,N/A. |
| MLA | Gao, Shanchuan,et al."Identification of tea plantations in typical plateau areas with the combination of Sentinel-1/2 optical and radar remote sensing data based on feature selection algorithm".INTERNATIONAL JOURNAL OF REMOTE SENSING N/A(2023). |
入库方式： OAI收割
来源：地理科学与资源研究所
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