Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest

doi:10.3390/rs10020242

	Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest
	Chen, Yuehong 1; Ge, Yong 2; An, Ru 1; Chen, Yu 3
刊名	REMOTE SENSING
	2018-02-01
卷号	10 期号:2 页码:16
关键词	super-resolution mapping impervious surfaces spatial dependence points of interest urban remote sensing
ISSN号	2072-4292
DOI	10.3390/rs10020242
通讯作者	Chen, Yuehong(chenyu@lreis.ac.cn) ; Ge, Yong(gey@lreis.ac.cn)
英文摘要	The accurate mapping of impervious surfaces is of key significance for various urban applications. Usually, traditional methods extract the proportion image of impervious surfaces from remote sensing images; however, the proportion image cannot specify where the impervious surfaces spatially distribute within a pixel. Meanwhile, impervious surfaces often locate urban areas and have a strong correlation with the relatively new big (geo)data points of interest (POIs). This study, therefore, proposed a novel impervious surfaces mapping method (super-resolution mapping of impervious surfaces, SRMIS) by combining a super-resolution mapping technique and POIs to increase the spatial resolution of impervious surfaces in proportion images and determine the accurate spatial location of impervious surfaces within each pixel. SRMIS was evaluated using a 10-m Sentinel-2 image and a 30-m Landsat 8 Operational Land Imager (OLI) image of Nanjing city, China. The experimental results show that SRMIS generated satisfactory impervious surface maps with better-classified image quality and greater accuracy than a traditional hard classifier, the two existing super-resolution mapping (SRM) methods of the subpixel-swapping algorithm, or the method using both pixel-level and subpixel-level spatial dependence. The experimental results show that the overall accuracy increase of SRMIS was from 2.34% to 5.59% compared with the hard classification method and the two SRM methods in the first experiment, while the overall accuracy of SRMIS was 1.34-3.09% greater than that of the compared methods in the second experiment. Hence, this study provides a useful solution to combining SRM techniques and the relatively new big (geo)data (i.e., POIs) to extract impervious surface maps with a higher spatial resolution than that of the input remote sensing images, and thereby supports urban research.
资助项目	National Natural Science Foundation of China[41701376] ; National Natural Science Foundation of China[41725006] ; Natural Science Foundation of Jiangsu Province[BK20170866] ; Key Program of Chinese Academy of Sciences[ZDRW-ZS-2016-6-3-4] ; Fundamental Research Funds for the Central Universities[2017B11714] ; China Postdoctoral Science Foundation[2016M600356] ; State Key Laboratory of Resources and Environmental Information System
WOS关键词	MARKOV-RANDOM-FIELD ; HOPFIELD NEURAL-NETWORK ; SENSING IMAGERY ; SUBPIXEL SCALE ; PIXEL ; RESOLUTION ; ALGORITHM ; INDICATOR ; MODEL ; CONSTRAINTS
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000427542100087
资助机构	National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province ; Key Program of Chinese Academy of Sciences ; Fundamental Research Funds for the Central Universities ; China Postdoctoral Science Foundation ; State Key Laboratory of Resources and Environmental Information System
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/57208]
专题	中国科学院地理科学与资源研究所
通讯作者	Chen, Yuehong; Ge, Yong
作者单位	1.Hohai Univ, Sch Earth Sci & Engn, Nanjing 210098, Jiangsu, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Nanjing Normal Univ, Sch Geog Sci, Nanjing 210023, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Chen, Yuehong,Ge, Yong,An, Ru,et al. Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest[J]. REMOTE SENSING,2018,10(2):16.
APA	Chen, Yuehong,Ge, Yong,An, Ru,&Chen, Yu.(2018).Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest.REMOTE SENSING,10(2),16.
MLA	Chen, Yuehong,et al."Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest".REMOTE SENSING 10.2(2018):16.