Semi automatic road extraction from digital images

Bakhtiari, HRR; Abdollahi, A; Rezaeian, H

Semi automatic road extraction from digital images

Bakhtiari, HRR Abdollahi, A

Rezaeian, H

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Egyptian Journal of Remote Sensing and Space Science, 2017, 20, (1), pp. 117-123
Issue Date:: 2017-06-01

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Field	Value	Language
dc.contributor.author	Bakhtiari, HRR
dc.contributor.author	Abdollahi, A https://orcid.org/0000-0002-1704-4670
dc.contributor.author	Rezaeian, H
dc.date.accessioned	2022-10-28T02:04:13Z
dc.date.available	2022-10-28T02:04:13Z
dc.date.issued	2017-06-01
dc.identifier.citation	Egyptian Journal of Remote Sensing and Space Science, 2017, 20, (1), pp. 117-123
dc.identifier.issn	1110-9823
dc.identifier.issn	2090-2476
dc.identifier.uri	http://hdl.handle.net/10453/162789
dc.description.abstract	Road extraction from digital images is of fundamental importance in the context of automatic mapping, effective urban planning and updating GIS databases. Very high spatial resolution (VHR) imagery acquired by airborne and space borne sensors is the main source for accurate road extraction. Manual techniques are fading away as they are time consuming and costly. Hence, road extraction method that is significantly more automated has become a research hotspot in remote sensing information processing. This paper proposes a semi-automatic approach to extract different road types from high-resolution remote sensing images. The approach is based on edge detection and SVM and mathematical morphology method. First the outline of the road is detected based on Canny operator. Then, Full Lambda Schedule merging method combines adjacent segments. Then the entire image was classified using Support Vector Machine (SVM) and various spatial, spectral, and texture attributes to form a road image. Finally, the quality of detected roads is improved using morphological operators. The algorithm was systematically evaluated on a variety of satellite images from Worldview, QuickBird and UltraCam airborne Images. The results of the accuracy evaluation demonstrate that the proposed road extraction approach can provide high accuracy for extraction of different road types.
dc.language	en
dc.publisher	Elsevier BV
dc.relation.ispartof	Egyptian Journal of Remote Sensing and Space Science
dc.relation.isbasedon	10.1016/j.ejrs.2017.03.001
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Semi automatic road extraction from digital images
dc.type	Journal Article
utslib.citation.volume	20
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	open_access	*
dc.date.updated	2022-10-28T02:04:03Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	20
utslib.citation.issue	1

Abstract:

Road extraction from digital images is of fundamental importance in the context of automatic mapping, effective urban planning and updating GIS databases. Very high spatial resolution (VHR) imagery acquired by airborne and space borne sensors is the main source for accurate road extraction. Manual techniques are fading away as they are time consuming and costly. Hence, road extraction method that is significantly more automated has become a research hotspot in remote sensing information processing. This paper proposes a semi-automatic approach to extract different road types from high-resolution remote sensing images. The approach is based on edge detection and SVM and mathematical morphology method. First the outline of the road is detected based on Canny operator. Then, Full Lambda Schedule merging method combines adjacent segments. Then the entire image was classified using Support Vector Machine (SVM) and various spatial, spectral, and texture attributes to form a road image. Finally, the quality of detected roads is improved using morphological operators. The algorithm was systematically evaluated on a variety of satellite images from Worldview, QuickBird and UltraCam airborne Images. The results of the accuracy evaluation demonstrate that the proposed road extraction approach can provide high accuracy for extraction of different road types.

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http://hdl.handle.net/10453/162789