Discrimination of the degree of heavy metal pollution in corn leaves in mining areas based on semi-supervised learning
文献类型：期刊论文
| 作者 | He, Jiale3; Yang, Keming2,3; Yang, Fei1; Li, Yanru3; Wu, Bing3; Zhang, Jianhong3 |
| 刊名 | REMOTE SENSING LETTERS
 |
| 出版日期 | 2024-01-02 |
| 卷号 | 15期号:1页码:10-23 |
| ISSN号 | 2150-704X |
| 关键词 | Corn leaves heavy metal pollution hyperspectral semi-supervised learning discriminant model |
| DOI | 10.1080/2150704X.2023.2293473 |
| 通讯作者 | Yang, Keming(ykm69@163.com) |
| 英文摘要 | To assess the degree of heavy metal pollution in crops, this study focused on crop leaves subjected to varying levels of heavy metal copper (Cu) stress. After removing outliers and applying smoothing techniques, the spectral data underwent derivative (D) and multiplicative scatter correction (MSC). Competitive adaptive reweighted sampling (CARS) and specific spectral ranges (SSR) were employed to extract the feature bands. Six different combinations of data preprocessing methods were utilized. This model incorporates the semi-supervised learning method, which enables the reduction of time and cost associated with annotating large-scale data. It addresses the identification problem when the labelled data is limited. Finally, the eXtreme Gradient Boosting algorithm (XGBoost) is used to select the best model and discriminate the degree of heavy metal pollution in corn. The D-CARS-XGBoost model achieved an accuracy rate exceeding 98%. |
| 资助项目 | Science & Technology Fundamental Resources Investigation Program[2022FY101905] ; National Natural Science Foundation of China[41971401] ; Fundamental Research Funds for the Central Universities[2022YJSDC22] |
| WOS研究方向 | Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | TAYLOR & FRANCIS LTD |
| WOS记录号 | WOS:001130432100001 |
| 资助机构 | Science & Technology Fundamental Resources Investigation Program ; National Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/201443]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Yang, Keming |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 2.China Univ Min & Technol Beijing, Sch Earth Sci & Surveying & Mapping Engn, Xueyuan Rd, Beijing, Peoples R China 3.China Univ Min & Technol Beijing, Sch Earth Sci & Surveying & Mapping Engn, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | He, Jiale,Yang, Keming,Yang, Fei,et al. Discrimination of the degree of heavy metal pollution in corn leaves in mining areas based on semi-supervised learning[J]. REMOTE SENSING LETTERS,2024,15(1):10-23. |
| APA | He, Jiale,Yang, Keming,Yang, Fei,Li, Yanru,Wu, Bing,&Zhang, Jianhong.(2024).Discrimination of the degree of heavy metal pollution in corn leaves in mining areas based on semi-supervised learning.REMOTE SENSING LETTERS,15(1),10-23. |
| MLA | He, Jiale,et al."Discrimination of the degree of heavy metal pollution in corn leaves in mining areas based on semi-supervised learning".REMOTE SENSING LETTERS 15.1(2024):10-23. |
入库方式： OAI收割
来源：地理科学与资源研究所
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