IGSNRR OpenIR
Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme
Cai, Guoyin1,2; Ren, Huiqun1; Yang, Liuzhong3; Zhang, Ning3,4; Du, Mingyi1,2; Wu, Changshan1,2,5
2019-07-12
Source PublicationSENSORS
ISSN1424-8220
Volume19Issue:14Pages:24
Corresponding AuthorWu, Changshan(cswu@uwm.edu)
AbstractUrban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper (TM) data, cannot uncover detailed LULC information. Further, very high resolution (VHR) satellite imagery, such as IKONOS and QuickBird data, can only be applied to a small area, largely due to the data unavailability and high computation cost. As a result, little research has been conducted to extract detailed urban LULC information for a large urban area. This study, therefore, developed a three-layer classification scheme for deriving detailedurban LULC information by integrating newly launched Chinese GF-1 (medium resolution) and GF-2 (very high resolution) satellite imagery and synthetically incorporating geometry, texture, and spectral information through multi-resolution image segmentation and object-based image classification (OBIA). Homogeneous urban LULC types such as water bodies or large areas of vegetation could be derived from GF-1 imagery with 16 m and 8 m spatial resolutions, while heterogeneous urban LULC types such as industrial buildings, residential buildings, and roads could be extracted from GF-2 imagery with 3.2 m and 0.8 m spatial resolutions. The multi-resolution segmentation method and a random forest algorithm were employed to perform image segmentation and object-based image classification, respectively. An analysis of the results suggests an overall accuracy of 0.89 and 0.87 were achieved for the second and third level urban LULC classification maps, respectively. Therefore, the three-layer classification scheme has the potential to derive high accuracy urban LULC information through integrating medium and high-resolution remote sensing imagery.
Keywordurban land use land cover three-layer classification scheme GF-1 satellite imagery GF-2 satelliteimagery
DOI10.3390/s19143120
WOS KeywordMACHINE-LEARNING ALGORITHMS ; IMAGE-ANALYSIS ; ENVIRONMENTAL-CHANGE ; SATELLITE IMAGERY ; ACCURACY ; EXTRACTION ; GEOBIA ; CHINA ; REGION ; AREAS
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2016YFB0501404] ; Land Use Change Detection from Satellite GF-7 by Ministry of Housing and Urban-Rural Development of the Peoples Republic of China[06-Y20A17-9001-17/18] ; Science and Technology Plans of Ministry of Housing and Urban-Rural Development of the Peoples Republic of China ; Opening projects of Beijing Advanced Innovation Centre for Future Urban Design, Beijing University of Civil Engineering and Architectural[VDC2017021422]
Funding OrganizationNational Key Research and Development Program of China ; Land Use Change Detection from Satellite GF-7 by Ministry of Housing and Urban-Rural Development of the Peoples Republic of China ; Science and Technology Plans of Ministry of Housing and Urban-Rural Development of the Peoples Republic of China ; Opening projects of Beijing Advanced Innovation Centre for Future Urban Design, Beijing University of Civil Engineering and Architectural
WOS Research AreaChemistry ; Electrochemistry ; Instruments & Instrumentation
WOS SubjectChemistry, Analytical ; Electrochemistry ; Instruments & Instrumentation
WOS IDWOS:000479160300087
PublisherMDPI
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/68727
Collection中国科学院地理科学与资源研究所
Corresponding AuthorWu, Changshan
Affiliation1.Beijing Univ Civil Engn & Architecture, Sch Geomat & Urban Spatial Informat, Beijing 100044, Peoples R China
2.Beijing Univ Civil Engn & Architecture, Beijing Adv Innovat Ctr Future Urban Design, Beijing 100044, Peoples R China
3.Minist Housing & Urban Rural Dev Peoples Republ C, Remote Sensing Applicat Ctr, Beijing 100835, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 101408, Peoples R China
5.Univ Wisconsin Milwaukee, Dept Geog, Milwaukee, WI 53211 USA
Recommended Citation
GB/T 7714
Cai, Guoyin,Ren, Huiqun,Yang, Liuzhong,et al. Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme[J]. SENSORS,2019,19(14):24.
APA Cai, Guoyin,Ren, Huiqun,Yang, Liuzhong,Zhang, Ning,Du, Mingyi,&Wu, Changshan.(2019).Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme.SENSORS,19(14),24.
MLA Cai, Guoyin,et al."Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme".SENSORS 19.14(2019):24.
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