Road Extraction from High-Resolution Orthophoto Images Using Convolutional Neural Network

Abdollahi, A; Pradhan, B; Shukla, N

Road Extraction from High-Resolution Orthophoto Images Using Convolutional Neural Network

Abdollahi, A

Pradhan, B

Shukla, N

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Journal of the Indian Society of Remote Sensing, 2021, 49, (3), pp. 569-583
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Abdollahi, A https://orcid.org/0000-0002-1704-4670
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Shukla, N https://orcid.org/0000-0002-8421-3972
dc.date.accessioned	2021-05-05T00:57:10Z
dc.date.available	2021-05-05T00:57:10Z
dc.date.issued	2021-01-01
dc.identifier.citation	Journal of the Indian Society of Remote Sensing, 2021, 49, (3), pp. 569-583
dc.identifier.issn	0255-660X
dc.identifier.issn	0974-3006
dc.identifier.uri	http://hdl.handle.net/10453/148656
dc.description.abstract	© 2020, Indian Society of Remote Sensing. Abstract: Two of the major applications in geospatial information system (GIS) and remote sensing fields are object detection and man-made feature extraction (e.g., road sections) from high-resolution remote sensing imagery. Extracting roads from high-resolution remotely sensed imagery plays a crucial role in multiple applications, such as navigation, emergency tasks, land cover change detection, and updating GIS maps. This study presents a deep learning technique based on a convolutional neural network (CNN) to classify and extract roads from orthophoto images. We applied the model on five orthophoto images to specify the superiority of the method for road extraction. First, we used principal component analysis and object-based image analysis for pre-processing to not only obtain spectral information but also add spatial and textural information for enhancing the classification accuracy. Then, the obtained results from the previous step were used as input for the CNN model to classify the images into road and non-road parts and trivial opening and closing operation are applied to extract connected road components from the images and remove holes inside the road parts. For the accuracy assessment of the proposed method, we used measurement factors such as precision, recall, F1 score, overall accuracy, and IOU. Achieved results showed that the average percentages of these factors were 91.09%, 95.32%, 93.15%, 94.44%, and 87.21%. The results were also compared with those of other existing methods. The comparison ascertained the reliability and superior performance of the suggested model architecture for extracting road regions from orthophoto images. Graphic Abstract: [Figure not available: see fulltext.]
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Journal of the Indian Society of Remote Sensing
dc.relation.isbasedon	10.1007/s12524-020-01228-y
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in [Journal of the Indian Society of Remote Sensing, 2021, 49, (3), pp. 569-583]. The final authenticated version is available online at: [ doi.org/10.1007/s12524-020-01228-y]”
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	Road Extraction from High-Resolution Orthophoto Images Using Convolutional Neural Network
dc.type	Journal Article
utslib.citation.volume	49
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/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Strength - PERSWADE - Centre on Persuasive Systems for Wise Adaptive Living
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	recently_added	*
pubs.consider-herdc	false
dc.date.updated	2021-05-05T00:57:08Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	49
utslib.citation.issue	3

Abstract:

© 2020, Indian Society of Remote Sensing. Abstract: Two of the major applications in geospatial information system (GIS) and remote sensing fields are object detection and man-made feature extraction (e.g., road sections) from high-resolution remote sensing imagery. Extracting roads from high-resolution remotely sensed imagery plays a crucial role in multiple applications, such as navigation, emergency tasks, land cover change detection, and updating GIS maps. This study presents a deep learning technique based on a convolutional neural network (CNN) to classify and extract roads from orthophoto images. We applied the model on five orthophoto images to specify the superiority of the method for road extraction. First, we used principal component analysis and object-based image analysis for pre-processing to not only obtain spectral information but also add spatial and textural information for enhancing the classification accuracy. Then, the obtained results from the previous step were used as input for the CNN model to classify the images into road and non-road parts and trivial opening and closing operation are applied to extract connected road components from the images and remove holes inside the road parts. For the accuracy assessment of the proposed method, we used measurement factors such as precision, recall, F1 score, overall accuracy, and IOU. Achieved results showed that the average percentages of these factors were 91.09%, 95.32%, 93.15%, 94.44%, and 87.21%. The results were also compared with those of other existing methods. The comparison ascertained the reliability and superior performance of the suggested model architecture for extracting road regions from orthophoto images. Graphic Abstract: [Figure not available: see fulltext.]

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http://hdl.handle.net/10453/148656