Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme

doi:10.3390/s19143120

	Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme
	Cai, Guoyin 1,2; Ren, Huiqun 1; Yang, Liuzhong 3; Zhang, Ning 3,4; Du, Mingyi 1,2; Wu, Changshan 1,2,5
刊名	SENSORS
	2019-07-12
卷号	19 期号:14 页码:24
关键词	urban land use land cover three-layer classification scheme GF-1 satellite imagery GF-2 satelliteimagery
ISSN号	1424-8220
DOI	10.3390/s19143120
通讯作者	Wu, Changshan(cswu@uwm.edu)
英文摘要	Urban 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.
资助项目	National 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]
WOS关键词	MACHINE-LEARNING ALGORITHMS ; IMAGE-ANALYSIS ; ENVIRONMENTAL-CHANGE ; SATELLITE IMAGERY ; ACCURACY ; EXTRACTION ; GEOBIA ; CHINA ; REGION ; AREAS
WOS研究方向	Chemistry ; Electrochemistry ; Instruments & Instrumentation
语种	英语
出版者	MDPI
WOS记录号	WOS:000479160300087
资助机构	National 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
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/68726]
专题	中国科学院地理科学与资源研究所
通讯作者	Wu, Changshan
作者单位	1.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
推荐引用方式 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.