A Novel Two-Step Framework for Mapping Fraction of Mulched Film Based on Very-High-Resolution Satellite Observation and Deep Learning
文献类型：期刊论文
| 作者 | Wei, Zhihao5,6; Cui, Yaokui5,6; Li, Sien4; Wang, Xuhui3; Dong, Jinwei2; Wu, Lifeng1; Yao, Zhaoyuan5,6; Wang, Shangjin4; Fan, Wenjie |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2024 |
| 卷号 | 62页码:14 |
| 关键词 | Agricultural plastic mulch deep learning fraction of mulched film remote sensing very high resolution |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2024.3382352 |
| 英文摘要 | The fraction of mulched film is of great significance for evaluating the agricultural water-saving effect and controlling environmental plastic pollution. Unfortunately, there is no work that has been done to obtain this parameter due to the mixed pixel issue of satellite imagery with medium and low resolutions, until now. In this study, we proposed a novel two-step framework for mapping fraction of mulched film based on very-high-resolution satellite observation and deep learning. The first step is extracting the extent of the mulched film based on a new few-shot learning model named parameter transition convolutional neural network (PT-CNN), which aims to increase the extraction accuracy and overcome the lack of labeled training data. The second step is retrieving the fraction of the mulched film at pixel scale based on a spectrum analysis method. The result shows that the proposed PT-CNN outperforms several state-of-the-art methods in mulched film extracting, with F1-scores of 97.09% and 98.65% for white and black mulched film, respectively. Meanwhile, the retrieved pixel scale fraction of mulched film shows high consistency to in situ measurement, with mean absolute error (MAE) of 0.0321. The proposed method can be useful in agricultural water resource management and environmental governance. |
| WOS关键词 | PLASTIC MULCH ; CROP EVAPOTRANSPIRATION ; WATER-USE ; YIELD ; GREENHOUSE ; EXTRACTION ; MACHINE ; IMAGES |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001205808000028 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/205889]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Cui, Yaokui |
| 作者单位 | 1.Nanchang Inst Technol, Sch Hydraul & Ecol Engn, Nanchang 330099, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Peking Univ, Coll Urban & Environm Sci, Sino French Inst Earth Syst Sci, Beijing 100871, Peoples R China 4.China Agr Univ, Coll Water Conservancy & Civil Engn, Beijing 100083, Peoples R China 5.Peking Univ, Beijing Key Lab Spatial Informat Integrat & Applic, Beijing 100871, Peoples R China 6.Peking Univ, Sch Earth & Space Sci, Inst Remote Sensing & Geog Informat Syst, Beijing 100871, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wei, Zhihao,Cui, Yaokui,Li, Sien,et al. A Novel Two-Step Framework for Mapping Fraction of Mulched Film Based on Very-High-Resolution Satellite Observation and Deep Learning[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2024,62:14. |
| APA | Wei, Zhihao.,Cui, Yaokui.,Li, Sien.,Wang, Xuhui.,Dong, Jinwei.,...&Fan, Wenjie.(2024).A Novel Two-Step Framework for Mapping Fraction of Mulched Film Based on Very-High-Resolution Satellite Observation and Deep Learning.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,62,14. |
| MLA | Wei, Zhihao,et al."A Novel Two-Step Framework for Mapping Fraction of Mulched Film Based on Very-High-Resolution Satellite Observation and Deep Learning".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 62(2024):14. |
入库方式： OAI收割
来源：地理科学与资源研究所
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