Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/19511
Title: Spectral-spatial classification and shape features for urban road centerline extraction
Authors: Shi, W 
Miao, Z
Wang, Q
Zhang, H
Keywords: High-resolution remotely sensed imagery
Local Geary's C
Main road extraction
Path openings and closings
Shape features
Spectral-spatial classification
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE geoscience and remote sensing letters, 2014, v. 11, no. 4, 6594858, p. 788-792 How to cite?
Journal: IEEE geoscience and remote sensing letters 
Abstract: This letter presents a two-step method for urban main road extraction from high-resolution remotely sensed imagery by integrating spectral-spatial classification and shape features. In the first step, spectral-spatial classification segments the imagery into two classes, i.e., the road class and the nonroad class, using path openings and closings. The local homogeneity of the gray values obtained by local Geary's C is then fused with the road class. In the second step, the road class is refined by using shape features. The experimental results indicated that the proposed method was able to achieve a comparatively good performance in urban main road extraction.
URI: http://hdl.handle.net/10397/19511
ISSN: 1545-598X
DOI: 10.1109/LGRS.2013.2279034
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