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Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest
Chen, Yuehong1; Ge, Yong2; An, Ru1; Chen, Yu3
2018-02-01
Source PublicationREMOTE SENSING
ISSN2072-4292
Volume10Issue:2Pages:16
Corresponding AuthorChen, Yuehong(chenyu@lreis.ac.cn) ; Ge, Yong(gey@lreis.ac.cn)
AbstractThe 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.
Keywordsuper-resolution mapping impervious surfaces spatial dependence points of interest urban remote sensing
DOI10.3390/rs10020242
WOS KeywordMARKOV-RANDOM-FIELD ; HOPFIELD NEURAL-NETWORK ; SENSING IMAGERY ; SUBPIXEL SCALE ; PIXEL ; RESOLUTION ; ALGORITHM ; INDICATOR ; MODEL ; CONSTRAINTS
Indexed BySCI
Language英语
Funding ProjectNational 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
Funding OrganizationNational 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
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000427542100087
PublisherMDPI
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/57209
Collection中国科学院地理科学与资源研究所
Corresponding AuthorChen, Yuehong; Ge, Yong
Affiliation1.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
Recommended Citation
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.
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