Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/13588
Title: An integrated method for urban main-road centerline extraction from optical remotely sensed imagery
Authors: Shi, W 
Miao, Z
Debayle, J
Keywords: General adaptive neighborhood (GAN)
Local Geary's C
Local linear kernel smoothing regression
Optical remotely sensed images
Shape feature
Spectral-spatial classification
Tensor voting
Urban main-road centerline extraction
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on geoscience and remote sensing, 2014, v. 52, no. 6, 6567905, p. 3359-3372 How to cite?
Journal: IEEE transactions on geoscience and remote sensing 
Abstract: Road information has a fundamental role in modern society. Road extraction from optical satellite images is an economic and efficient way to obtain and update a transportation database. This paper presents an integrated method to extract urban main-road centerlines from satellite optical images. The proposed method has four main steps. First, general adaptive neighborhood is introduced to implement spectral-spatial classification to segment the images into two categories: road and nonroad groups. Second, road groups and homogeneous property, measured by local Geary's C, are fused to improve road-group accuracy. Third, road shape features are used to extract reliable road segments. Finally, local linear kernel smoothing regression is performed to extract smooth road centerlines. Road networks are then generated using tensor voting. The proposed method is tested and subsequently validated using a large set of multispectral high-resolution images. A comparison with several existing methods shows that the proposed method is more suitable for urban main-road centerline extraction.
URI: http://hdl.handle.net/10397/13588
ISSN: 0196-2892
EISSN: 1558-0644
DOI: 10.1109/TGRS.2013.2272593
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