Mapping US Urban Extents from MODIS Data Using One-Class Classification Method

Remote Sensing

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, i.e., positive and unlabeled learning (PUL), with multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data for the year 2010. The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) night stable light data were used to calibrate the urban extents obtained from the one-class classification scheme. Our results demonstrated the effectiveness of the use of the PUL algorithm in mapping large-scale urban areas from coarse remote-sensing images, for the first time. The total accuracy of mapped urban areas was 92.9% and the kappa coefficient was 0.85. The use of DMSP-OLS night stable light data can significantly reduce false detection rates from bare land and cropland far from cities. Compared with traditional supervised classification methods, the one-class classification scheme can greatly reduce the effort involved in collecting training datasets, without losing predictive accuracy.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.