Investigation of SVM and Level Set Interactive Methods for Road Extraction from Google Earth Images

Abdollahi, A; Bakhtiari, HRR; Nejad, MP

Investigation of SVM and Level Set Interactive Methods for Road Extraction from Google Earth Images

Abdollahi, A

Bakhtiari, HRR Nejad, MP

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Journal of the Indian Society of Remote Sensing, 2018, 46, (3), pp. 423-430
Issue Date:: 2018-03-01

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Field	Value	Language
dc.contributor.author	Abdollahi, A https://orcid.org/0000-0002-1704-4670
dc.contributor.author	Bakhtiari, HRR
dc.contributor.author	Nejad, MP
dc.date.accessioned	2022-10-29T00:58:57Z
dc.date.available	2022-10-29T00:58:57Z
dc.date.issued	2018-03-01
dc.identifier.citation	Journal of the Indian Society of Remote Sensing, 2018, 46, (3), pp. 423-430
dc.identifier.issn	0255-660X
dc.identifier.issn	0974-3006
dc.identifier.uri	http://hdl.handle.net/10453/162851
dc.description.abstract	Currently, methods of extracting spatial information from satellite images are mainly based on visual interpretations and drawing the consequences by human factor, which is both costly and time consuming. A large volume of data collected by satellite sensors, and significant improvement in spatial and spectral resolution of these images require the development of new methods for optimal use of these data in order to produce rapid economic and updating road maps. In this study, a new automatic method is proposed for road extraction by integrating the SVM and Level Set methods. The estimated probability of classification by SVM is used as input in Level Set Method. The average of completeness, correctness, and quality was 84.19, 88.69 and 76.06% respectively indicate high performance of proposed method for road extraction from Google Earth images.
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Journal of the Indian Society of Remote Sensing
dc.relation.isbasedon	10.1007/s12524-017-0702-x
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0406 Physical Geography and Environmental Geoscience, 0801 Artificial Intelligence and Image Processing, 0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.title	Investigation of SVM and Level Set Interactive Methods for Road Extraction from Google Earth Images
dc.type	Journal Article
utslib.citation.volume	46
utslib.for	0406 Physical Geography and Environmental Geoscience
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0909 Geomatic Engineering
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	closed_access	*
dc.date.updated	2022-10-29T00:58:56Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	46
utslib.citation.issue	3

Abstract:

Currently, methods of extracting spatial information from satellite images are mainly based on visual interpretations and drawing the consequences by human factor, which is both costly and time consuming. A large volume of data collected by satellite sensors, and significant improvement in spatial and spectral resolution of these images require the development of new methods for optimal use of these data in order to produce rapid economic and updating road maps. In this study, a new automatic method is proposed for road extraction by integrating the SVM and Level Set methods. The estimated probability of classification by SVM is used as input in Level Set Method. The average of completeness, correctness, and quality was 84.19, 88.69 and 76.06% respectively indicate high performance of proposed method for road extraction from Google Earth images.

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http://hdl.handle.net/10453/162851