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Open AccessArticle

Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting

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School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1439957131, Iran
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Faculty of Geomatics, Computer Science and Mathematics, University of Applied Sciences, Schellingstr. 24, Stuttgart 70174, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Devrim Akca, Gonzalo Pajares Martinsanz and Prasad S. Thenkabail
Remote Sens. 2016, 8(8), 637; https://doi.org/10.3390/rs8080637
Received: 12 May 2016 / Revised: 16 July 2016 / Accepted: 26 July 2016 / Published: 4 August 2016
Road networks are very important features in geospatial databases. Even though high-resolution optical satellite images have already been acquired for more than a decade, tools for automated extraction of road networks from these images are still rare. One consequence of this is the need for manual interaction which, in turn, is time and cost intensive. In this paper, a multi-stage approach is proposed which integrates structural, spectral, textural, as well as contextual information of objects to extract road networks from very high resolution satellite images. Highlights of the approach are a novel linearity index employed for the discrimination of elongated road segments from other objects and customized tensor voting which is utilized to fill missing parts of the network. Experiments are carried out with different datasets. Comparison of the achieved results with the results of seven state-of-the-art methods demonstrated the efficiency of the proposed approach. View Full-Text
Keywords: road extraction; shape analysis; object linearity index; tensor voting; guided filter road extraction; shape analysis; object linearity index; tensor voting; guided filter
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MDPI and ACS Style

Maboudi, M.; Amini, J.; Hahn, M.; Saati, M. Road Network Extraction from VHR Satellite Images Using Context Aware Object Feature Integration and Tensor Voting. Remote Sens. 2016, 8, 637.

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