Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/55934
Title: An object-based method for road network extraction in VHR satellite images
Authors: Miao, Z
Shi, WZ 
Gamba, P
Li, Z
Keywords: Tensor voting (TV)
Active contour
Object-based filters
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE journal of selected topics in applied earth observations and remote sensing, 2015, v. 8, no. 10, p. 4853-4862 How to cite?
Journal: IEEE journal of selected topics in applied earth observations and remote sensing 
Abstract: Road extraction from very high-resolution (VHR) satellite images plays an important role in the remote-sensing applications. Although tracing road features in satellite images has received much attention over the years, fully automated methods appear to be in their infancy. To tackle these limitations to some extent, this paper presents a novel object-based automatic road extraction method. The proposed method consists of five main steps. First, satellite images are segmented to generate objects. Then, two object-based filters are applied to compute object features to select road candidates. After that, the road class is extracted using the support vector machine (SVM) based on the extracted feature set. Finally, tensor voting (TV), active contour, and the geometrical information are integrated to eliminate road gaps and improve road smoothness. Experiments are conducted on nine test sites. It is experimentally demonstrated that the proposed method produces an excellent accuracy for the automatic road extraction from VHR satellite images.
URI: http://hdl.handle.net/10397/55934
ISSN: 1939-1404
EISSN: 2151-1535
DOI: 10.1109/JSTARS.2015.2443552
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