Remote Sensing | |
Mapping US Urban Extents from MODIS Data Using One-Class Classification Method | |
Bo Wan2  Qinghua Guo3  Fang Fang2  Yanjun Su3  Run Wang2  Ruiliang Pu1  | |
[1] Faculty of Information Engineering, China University of Geosciences, No. 388 Lumo Road, Wuhan 430074, China;;Faculty of Information Engineering, China University of Geosciences, No. 388 Lumo Road, Wuhan 430074, China; E-Mails:;Sierra Nevada Research Institute, School of Engineering, University of California at Merced, 5200 North Lake Road, Merced, CA 95343, USA; E-Mail: | |
关键词: urban mapping; remote sensing; MODIS; classification; time series; one-class; | |
DOI : 10.3390/rs70810143 | |
来源: mdpi | |
【 摘 要 】
Urban areas are one of the most important components of human society. Their extents have been continuously growing during the last few decades. Accurate and timely measurements of the extents of urban areas can help in analyzing population densities and urban sprawls and in studying environmental issues related to urbanization. Urban extents detected from remotely sensed data are usually a by-product of land use classification results, and their interpretation requires a full understanding of land cover types. In this study, for the first time, we mapped urban extents in the continental United States using a novel one-class classification method,
【 授权许可】
CC BY
© 2015 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
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RO202003190008291ZK.pdf | 2172KB | download |