IGSNRR OpenIR
Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network
Jia, Yuanxin1,2; Ge, Yong1,2; Chen, Yuehong3; Li, Sanping4; Heuvelink, Gerard B. M.5; Ling, Feng6
2019-08-01
Source PublicationREMOTE SENSING
Volume11Issue:15Pages:17
Corresponding AuthorGe, Yong(gey@lreis.ac.cn)
AbstractSuper-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.
Keywordsuper-resolution mapping land cover convolutional neural network remote sensing imagery
DOI10.3390/rs11151815
WOS KeywordPIXEL-SWAPPING ALGORITHM ; REMOTELY-SENSED IMAGES ; SCENE CLASSIFICATION ; SENTINEL-2 IMAGES ; INFORMATION ; MULTISCALE ; SERIES
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation for Distinguished Young Scholars of China[41725006] ; National Science Foundation of China[41531174] ; National Science Foundation of China[41531179]
Funding OrganizationNational Natural Science Foundation for Distinguished Young Scholars of China ; National Science Foundation of China
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000482442800079
PublisherMDPI
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/69614
Collection中国科学院地理科学与资源研究所
Corresponding AuthorGe, Yong
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Hohai Univ, Sch Earth Sci & Engn, Nanjing 210098, Jiangsu, Peoples R China
4.DELLEMC CTO TRIGr, Beijing 100084, Peoples R China
5.Wageningen Univ, Soil Geog & Landscape Grp, POB 47, NL-6700 AA Wageningen, Netherlands
6.Chinese Acad Sci, Inst Geodesy & Geophys, Wuhan 430077, Hubei, Peoples R China
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Jia, Yuanxin]'s Articles
[Ge, Yong]'s Articles
[Chen, Yuehong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Jia, Yuanxin]'s Articles
[Ge, Yong]'s Articles
[Chen, Yuehong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Jia, Yuanxin]'s Articles
[Ge, Yong]'s Articles
[Chen, Yuehong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.