Regional Urban Extent Extraction Using Multi-Sensor Data and One-Class Classification

Remote Sensing

Xiya Zhang¹  Peijun Li²  Cai Cai¹  Ioannis Gitas¹ 

[1] Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, China;

关键词: urban extent;    OCSVM;    DMSP/OLS;    NDVI;    land surface temperature;    one-class classification;   

DOI  :  10.3390/rs70607671

来源: mdpi

【 摘 要 】

Stable night-time light data from the Defense Meteorological Satellite Program (DMSP) Operational Line-scan System (OLS) provide a unique proxy for anthropogenic development. This paper presents a regional urban extent extraction method using a one-class classifier and combinations of DMSP/OLS stable night-time light (NTL) data, MODIS normalized difference vegetation index (NDVI) data, and land surface temperature (LST) data. We first analyzed how well MODIS NDVI and LST data quantify the properties of urban areas. Considering that urban area is the only class of interest, we applied the one-class support vector machine (OCSVM) to classify different combinations of the three datasets. We evaluated the effectiveness of the proposed method and compared with the locally optimized threshold method in regional urban extent mapping in China. The experimental results demonstrate that DMSP/OLS NTL data, MODIS NDVI and LST data provide different but complementary information sources to quantify the urban extent at a regional scale. The results also indicate that the OCSVM classification of the combination of all three datasets generally outperformed the locally optimized threshold method. The proposed method effectively and efficiently extracted the urban extent at a regional scale, and is applicable to other study areas.

【 授权许可】

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