Mapping Rice Paddy Distribution Using Remote Sensing by Coupling Deep Learning with Phenological Characteristics
文献类型：期刊论文
| 作者 | Zhu, A-Xing1,2,3,4,5 ; Zhao, Fang-He2,4; Pan, Hao-Bo1; Liu, Jun-Zhi1
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| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2021-04-01 |
| 卷号 | 13期号:7页码:16 |
| 关键词 | rice paddy distribution remote sensing phenological characteristics deep learning machine learning spatial prediction |
| DOI | 10.3390/rs13071360 |
| 通讯作者 | Zhao, Fang-He(zhaofh@lreis.ac.cn) |
| 英文摘要 | Two main approaches are used in mapping rice paddy distribution from remote sensing images: phenological methods or machine learning methods. The phenological methods can map rice paddy distribution in a simple way but with limited accuracy. Machine learning, particularly deep learning, methods that learn the spectral signatures can achieve higher accuracy yet require a large number of field samples. This paper proposed a pheno-deep method to couple the simplicity of the phenological methods and the learning ability of the deep learning methods for mapping rice paddy at high accuracy without the need of field samples. The phenological method was first used to initially delineate the rice paddy for the purpose of creating training samples. These samples were then used to train the deep learning model. The trained deep learning model was applied to map the spatial distribution of rice paddy. The effectiveness of the pheno-deep method was evaluated in Jin'an District, Lu'an City, Anhui Province, China. Results show that the pheno-deep method achieved a high performance with the overall accuracy, the precision, the recall, and AUC (area under curve) being 88.8%, 87.2%, 91.1%, and 94.4%, respectively. The pheno-deep method achieved a much better performance than the phenological alone method and can overcome the noises in the training samples from the phenological method. The overall accuracy of the pheno-deep method is only 2.4% lower than that of the deep learning alone method trained with field samples and this difference is not statistically significant. In addition, the pheno-deep method requires no field sampling, which would be a noteworthy advantage for situations when large training samples are difficult to obtain. This study shows that by combining knowledge-based methods with data-driven methods, it is possible to achieve high mapping accuracy of geographic variables using remote sensing even with little field sampling efforts. |
| 资助项目 | National Natural Science Foundation of China[41871300] ; National Natural Science Foundation of China[41431177] ; PAPD (Priority Academic Program Development of Jiangsu Higher Education Institutions) ; China Scholarship Council[201904910630] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000638791300001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China ; PAPD (Priority Academic Program Development of Jiangsu Higher Education Institutions) ; China Scholarship Council |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/161810]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhao, Fang-He |
| 作者单位 | 1.Nanjing Normal Univ, Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Sch Geog, Nanjing 210023, Peoples R China 2.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Univ Wisconsin Madison, Dept Geog, Madison, WI 53706 USA 4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 5.Southern Univ Sci & Technol, Ctr Social Sci, Shenzhen 518055, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, A-Xing,Zhao, Fang-He,Pan, Hao-Bo,et al. Mapping Rice Paddy Distribution Using Remote Sensing by Coupling Deep Learning with Phenological Characteristics[J]. REMOTE SENSING,2021,13(7):16. |
| APA | Zhu, A-Xing,Zhao, Fang-He,Pan, Hao-Bo,&Liu, Jun-Zhi.(2021).Mapping Rice Paddy Distribution Using Remote Sensing by Coupling Deep Learning with Phenological Characteristics.REMOTE SENSING,13(7),16. |
| MLA | Zhu, A-Xing,et al."Mapping Rice Paddy Distribution Using Remote Sensing by Coupling Deep Learning with Phenological Characteristics".REMOTE SENSING 13.7(2021):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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