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Sensors 2009, 9(2), 1237-1258; doi:10.3390/s90201237

Combining Multiple Algorithms for Road Network Tracking from Multiple Source Remotely Sensed Imagery: a Practical System and Performance Evaluation

1
Key Laboratory of Mapping from Space of State Bureau of Surveying and Mapping, Chinese Academy of Surveying and Mapping, 16, Beitaiping Road, Haidian District, Beijing 100039, P.R. China
2
School of Resources and Environment, Wuhan University, 129, Luoyu Road, Wuchang District, Wuhan 430079, P.R. China
*
Author to whom correspondence should be addressed.
Received: 31 October 2008 / Revised: 20 February 2009 / Accepted: 24 February 2009 / Published: 24 February 2009
(This article belongs to the Special Issue Sensor Algorithms)
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Abstract

In light of the increasing availability of commercial high-resolution imaging sensors, automatic interpretation tools are needed to extractroad features. Currently, many approaches for road extraction are available, but it is acknowledged that there is no single method that would be successful in extracting all types of roads from any remotely sensed imagery. In this paper, a novel classification of roads is proposed, based on both the roads’ geometrical, radiometric properties and the characteristics of the sensors. Subsequently, a general road tracking framework is proposed, and one or more suitable road trackers are designed or combined for each type of roads. Extensive experiments are performed to extract roads from aerial/satellite imagery, and the results show that a combination strategy can automatically extract more than 60% of the total roads from very high resolution imagery such as QuickBird and DMC images, with a time-saving of approximately 20%, and acceptable spatial accuracy. It is proven that a combination of multiple algorithms is more reliable, more efficient and more robust for extracting road networks from multiple-source remotely sensed imagery than the individual algorithms. View Full-Text
Keywords: Semi-automatic; road tracking; profile matching; template matching; angular texture signature; parallelepiped classification; lane marking Semi-automatic; road tracking; profile matching; template matching; angular texture signature; parallelepiped classification; lane marking
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Lin, X.; Liu, Z.; Zhang, J.; Shen, J. Combining Multiple Algorithms for Road Network Tracking from Multiple Source Remotely Sensed Imagery: a Practical System and Performance Evaluation. Sensors 2009, 9, 1237-1258.

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