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Object-based urban change detection using high resolution SAR images
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.ORCID iD: 0000-0002-1135-4192
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
2015 (English)In: 2015 Joint Urban Remote Sensing Event, JURSE 2015, IEEE conference proceedings, 2015Conference paper, Published paper (Refereed)
Abstract [en]

In this study, the unsupervised detection of urban changes, based on high-spatial resolution SAR imagery, is approached using the object-oriented paradigm. Multidate images segmentation strategy was adopted to avoid the creation of sliver polygon. Following segmentation, a change image was generated by comparing objects' mean intensities using a modified version of the traditional ratio operator. Three different unsupervised thresholding algorithms - that is, Kittler-Illingworth algorithm, Otsu method, and outlier detection technique - are used to threshold the change image and generate a binary change map. Two TerraSAR-X SAR images acquired over Shanghai in August, 2008, and September, 2011, were used to test the methods. The results indicate that, compared with pixel-based, the object-based approach helps in improving the quality of the produced change maps. The results also show that the three unsupervised thresholding algorithms performed equally well.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2015.
Keyword [en]
Algorithms, Image segmentation, Remote sensing, Synthetic aperture radar, High spatial resolution, High-resolution SAR, Images segmentations, Object oriented paradigm, Outlier Detection, Unsupervised detection, Unsupervised thresholding, Urban change detection, Radar imaging
National Category
Remote Sensing
Identifiers
URN: urn:nbn:se:kth:diva-174781DOI: 10.1109/JURSE.2015.7120502ISI: 000380429700054Scopus ID: 2-s2.0-84938866826ISBN: 9781479966523 (print)OAI: oai:DiVA.org:kth-174781DiVA: diva2:877882
Conference
2015 Joint Urban Remote Sensing Event, JURSE 2015, 30 March 2015 through 1 April 2015
Note

QC 20151208

Available from: 2015-12-08 Created: 2015-10-07 Last updated: 2016-08-23Bibliographically approved

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Yousif, Osama

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf