Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network
文献类型：期刊论文
| 作者 | Jia, Yuanxin2,6; Ge, Yong2,6; Chen, Yuehong3; Li, Sanping5; Heuvelink, Gerard B. M.1; Ling, Feng4 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2019-08-01 |
| 卷号 | 11期号:15页码:17 |
| 关键词 | super-resolution mapping land cover convolutional neural network remote sensing imagery |
| DOI | 10.3390/rs11151815 |
| 通讯作者 | Ge, Yong(gey@lreis.ac.cn) |
| 英文摘要 | Super-resolution mapping (SRM) is used to obtain fine-scale land cover maps from coarse remote sensing images. Spatial attraction, geostatistics, and using prior geographic information are conventional approaches used to derive fine-scale land cover maps. As the convolutional neural network (CNN) has been shown to be effective in capturing the spatial characteristics of geographic objects and extrapolating calibrated methods to other study areas, it may be a useful approach to overcome limitations of current SRM methods. In this paper, a new SRM method based on the CNN (SRMCNN) is proposed and tested. Specifically, an encoder-decoder CNN is used to model the nonlinear relationship between coarse remote sensing images and fine-scale land cover maps. Two real-image experiments were conducted to analyze the effectiveness of the proposed method. The results demonstrate that the overall accuracy of the proposed SRMCNN method was 3% to 5% higher than that of two existing SRM methods. Moreover, the proposed SRMCNN method was validated by visualizing output features and analyzing the performance of different geographic objects. |
| WOS关键词 | PIXEL-SWAPPING ALGORITHM ; REMOTELY-SENSED IMAGES ; SCENE CLASSIFICATION ; SENTINEL-2 IMAGES ; INFORMATION ; MULTISCALE ; SERIES |
| 资助项目 | National Natural Science Foundation for Distinguished Young Scholars of China[41725006] ; National Science Foundation of China[41531174] ; National Science Foundation of China[41531179] |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:000482442800079 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation for Distinguished Young Scholars of China ; National Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/69613]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Ge, Yong |
| 作者单位 | 1.Wageningen Univ, Soil Geog & Landscape Grp, POB 47, NL-6700 AA Wageningen, Netherlands 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Hohai Univ, Sch Earth Sci & Engn, Nanjing 210098, Jiangsu, Peoples R China 4.Chinese Acad Sci, Inst Geodesy & Geophys, Wuhan 430077, Hubei, Peoples R China 5.DELLEMC CTO TRIGr, Beijing 100084, Peoples R China 6.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jia, Yuanxin,Ge, Yong,Chen, Yuehong,et al. Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network[J]. REMOTE SENSING,2019,11(15):17. |
| APA | Jia, Yuanxin,Ge, Yong,Chen, Yuehong,Li, Sanping,Heuvelink, Gerard B. M.,&Ling, Feng.(2019).Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network.REMOTE SENSING,11(15),17. |
| MLA | Jia, Yuanxin,et al."Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network".REMOTE SENSING 11.15(2019):17. |
入库方式： OAI收割
来源：地理科学与资源研究所
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