Detection of urban environments using advanced land observing satellite phased array type L-band synthetic aperture radar data through different classification techniques

Pradhan, B; Abdullahi, S; Seddighi, Y

Detection of urban environments using advanced land observing satellite phased array type L-band synthetic aperture radar data through different classification techniques

Pradhan, B

Abdullahi, S Seddighi, Y

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Publisher:: International Society for Optics and Photonics
Publication Type:: Journal Article
Citation:: Journal of Applied Remote Sensing, 2016, 10, (3), pp. 036029-036029
Issue Date:: 2016

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Field	Value	Language
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Abdullahi, S
dc.contributor.author	Seddighi, Y
dc.date.accessioned	2022-07-15T06:53:30Z
dc.date.available	2022-07-15T06:53:30Z
dc.date.issued	2016
dc.identifier.citation	Journal of Applied Remote Sensing, 2016, 10, (3), pp. 036029-036029
dc.identifier.issn	1931-3195
dc.identifier.issn	1931-3195
dc.identifier.uri	http://hdl.handle.net/10453/158947
dc.description.abstract	Urban environments are very dynamic phenomena, and it is essential to update urban-related information for various applications. In this regard, remotely sensed data have been utilized widely to extract and monitor urban land use and land cover changes. Particularly, synthetic aperture radar (SAR) data, due to several advantages of this technology in comparison to passive sensors, provides better performance especially in tropical regions. However, the methodological approaches for extraction of information from SAR images are another important task that needs to be considered appropriately. This paper attempts to investigate and compare the performance of different image classification techniques for extracting urban areas using advanced land observing satellite phased array type L-band synthetic aperture radar imagery. Several object-[such as rule based (RB), support vector machine (SVM) and K-nearest neighbor (K-NN)] and pixel-based [decision tree (DT)] classification techniques were implemented, and their results were compared in detail. The overall results indicated RB classification performed better than other techniques. Furthermore, DT method, due to its predefined rules, distinguished the land cover classes better than SVM and K-NN, which were based on training datasets. Nevertheless, this study confirms the potential of SAR data and object-based classification techniques in urban detection and land cover mapping.
dc.language	English
dc.publisher	International Society for Optics and Photonics
dc.relation.ispartof	Journal of Applied Remote Sensing
dc.relation.isbasedon	10.1117/1.JRS.10.036029
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0401 Atmospheric Sciences, 0406 Physical Geography and Environmental Geoscience, 0909 Geomatic Engineering
dc.subject.classification	Geological & Geomatics Engineering
dc.title	Detection of urban environments using advanced land observing satellite phased array type L-band synthetic aperture radar data through different classification techniques
dc.type	Journal Article
utslib.citation.volume	10
utslib.for	0401 Atmospheric Sciences
utslib.for	0406 Physical Geography and Environmental Geoscience
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 Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
dc.date.updated	2022-07-15T06:53:28Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	3

Abstract:

Urban environments are very dynamic phenomena, and it is essential to update urban-related information for various applications. In this regard, remotely sensed data have been utilized widely to extract and monitor urban land use and land cover changes. Particularly, synthetic aperture radar (SAR) data, due to several advantages of this technology in comparison to passive sensors, provides better performance especially in tropical regions. However, the methodological approaches for extraction of information from SAR images are another important task that needs to be considered appropriately. This paper attempts to investigate and compare the performance of different image classification techniques for extracting urban areas using advanced land observing satellite phased array type L-band synthetic aperture radar imagery. Several object-[such as rule based (RB), support vector machine (SVM) and K-nearest neighbor (K-NN)] and pixel-based [decision tree (DT)] classification techniques were implemented, and their results were compared in detail. The overall results indicated RB classification performed better than other techniques. Furthermore, DT method, due to its predefined rules, distinguished the land cover classes better than SVM and K-NN, which were based on training datasets. Nevertheless, this study confirms the potential of SAR data and object-based classification techniques in urban detection and land cover mapping.

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http://hdl.handle.net/10453/158947