Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network

doi:10.3390/rs11151815

	Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network
	Jia, Yuanxin 1,2; Ge, Yong 1,2; Chen, Yuehong 3; Li, Sanping 6; Heuvelink, Gerard B. M.5; Ling, Feng 4
刊名	REMOTE SENSING
	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.
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关键词	PIXEL-SWAPPING ALGORITHM ; REMOTELY-SENSED IMAGES ; SCENE CLASSIFICATION ; SENTINEL-2 IMAGES ; INFORMATION ; MULTISCALE ; SERIES
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000482442800079
资助机构	National Natural Science Foundation for Distinguished Young Scholars of China ; National Science Foundation of China
内容类型	期刊论文
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.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.Chinese Acad Sci, Inst Geodesy & Geophys, Wuhan 430077, Hubei, Peoples R China 5.Wageningen Univ, Soil Geog & Landscape Grp, POB 47, NL-6700 AA Wageningen, Netherlands 6.DELLEMC CTO TRIGr, Beijing 100084, 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.