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Multitemporal Spaceborn SAR Data For Change Detection In Urban Areas: A Case Study In Shanghai
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).ORCID iD: 0000-0002-1135-4192
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics (closed 20110301).
2009 (English)In: Proceedings, ISPRS Virtual Changing Globe for Visualization and Analysis / [ed] Jianya Gong,Qiming Zhou, 2009Conference paper, Published paper (Other academic)
Abstract [en]

The objective of this research is to perform automatic change detection within urban areas using multitemporal spaceborne SAR datain Shanghai. Two scenes of ENVISAT ASAR C-VV images were acquired in September, 2008 and one scene of ERS-2 SAR C-VVimage was acquired in September, 1999. A generalized version of Kittler Illingworth minimum-error thresholding algorithm, thattakes into account the non-Gaussianity of SAR images, was tested to automatically classify the SAR ratio image into two classes,change and no change. Two types of comparison operators were performed. First, the conventional ratio image was calculated in away that only increases in backscatter coefficient are detected. Second, a modified ratio operator that takes into accounts bothpositive and negative changes was also examined. Various probability density functions such as, Log normal, Generalized Gaussian,Nakagami ratio, and Weibull ratio were tested to model the distribution of the change and no change classes. An iterative refinementof the Log normal model is also applied to improve the resolution of the change map. The preliminary results showed that thisunsupervised change detection algorithm is very effective in detecting temporal changes in urban areas using SAR images. The bestchange detection result was obtained using Log normal model with modified ratio operator at 81.5%, which is over 10% high thanthat of the other three models tested. The initial findings indicated that change detection accuracy varies depending on how theassumed conditional class density function fits the histograms of change and no change classes.

Place, publisher, year, edition, pages
2009.
Keyword [en]
Change detection, SAR, Minimum-error thresholding, Ratio image, Modified ratio, Urban area
National Category
Remote Sensing
Identifiers
URN: urn:nbn:se:kth:diva-88901OAI: oai:DiVA.org:kth-88901DiVA: diva2:502550
Conference
ISPRS Wuhan 2009 Workshop Virtual Changing Globe for Visualization and Analysis. Wuhan, China. October 27-18, 2009
Note
QC 20120426Available from: 2012-02-14 Created: 2012-02-14 Last updated: 2012-04-26Bibliographically approved

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Yousif, Osama A.

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CiteExportLink to record
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  • apa
  • harvard1
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