Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/1821
AuthorsStramondo, S.* 
Bignami, C.* 
Chini, M.* 
Pierdicca, N.* 
Tertulliani, A.* 
TitleSatellite radar and optical remote sensing for earthquake damage detection: results from different case studies
Issue Date20-Oct-2006
Series/Report no.20/27 (2006)
DOI10.1080/01431160600675895
URIhttp://hdl.handle.net/2122/1821
KeywordsInSAR
damage detection
Optical data
Urban areas
Subject Classification04. Solid Earth::04.03. Geodesy::04.03.06. Measurements and monitoring 
04. Solid Earth::04.03. Geodesy::04.03.07. Satellite geodesy 
04. Solid Earth::04.03. Geodesy::04.03.09. Instruments and techniques 
04. Solid Earth::04.06. Seismology::04.06.06. Surveys, measurements, and monitoring 
04. Solid Earth::04.06. Seismology::04.06.11. Seismic risk 
AbstractIn case of a seismic event, a fast and draft damage map of the hit urban areas can be very useful, in particular when the epicentre of the earthquake is located in remote regions, or the main communication systems are damaged. Our aim is to analyse the capability of remote sensing techniques for damage detection in urban areas and to explore the combined use of radar (SAR) and optical satellite data. Two case studies have been proposed: Izmit (1999; Turkey) and Bam (2003; Iran). Both areas have been affected by strong earthquakes causing heavy and extended damage in the urban settlements close to the epicentre. Different procedures for damage assessment have been successfully tested, either to perform a pixel by pixel classification or to assess damage within homogeneous extended areas. We have compared change detection capabilities of different features extracted from optical and radar data, and analysed the potential of combining measurements at different frequency ranges. Regarding the Izmit case, SAR features alone have reached 70% of correct classification of damaged areas and 5 m panchromatic optical images have given 82%; the fusion of SAR and optical data raised up to 89% of correct pixel‐to‐pixel classification. The same procedures applied to the Bam test case achieved about 61% of correct classification from SAR alone, 70% from optical data, while data fusion reached 76%. The results of the correlation between satellite remote sensing and ground surveys data have been presented by comparing remotely change detection features averaged within homogeneous blocks of buildings with ground survey data.
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