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Remote Sens. 2015, 7(6), 7671-7694;

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

Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Peking University, Beijing 100871, China
Author to whom correspondence should be addressed.
Academic Editors: Ioannis Gitas and Prasad S. Thenkabail
Received: 9 February 2015 / Revised: 26 May 2015 / Accepted: 29 May 2015 / Published: 9 June 2015
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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. View Full-Text
Keywords: urban extent; OCSVM; DMSP/OLS; NDVI; land surface temperature; one-class classification urban extent; OCSVM; DMSP/OLS; NDVI; land surface temperature; one-class classification

Figure 1a

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhang, X.; Li, P.; Cai, C. Regional Urban Extent Extraction Using Multi-Sensor Data and One-Class Classification. Remote Sens. 2015, 7, 7671-7694.

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