Spectral and spatial classification of earthquake images by support vector selection and adaptation

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)

Abstract

The aim of this study is to extract homogenous and edge regions from a post-earthquake Quickbird satellite image with high resolution and to combine this spatial information with spectral information in classification of earthquake damage. In order to extract the homogenous and edge regions from the image, a spatial filtering approach and Canny filter were used. A novel method called support vector selection and adaptation (SVSA) was used in classification of earthquake damage. Pixel and texture-based classification were separately carried out in order to show their comparative classification performance. For implementation, a small region from city of Bam in Iran was selected.

Original languageEnglish
Title of host publicationProceedings of the 2010 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2010
Pages194-197
Number of pages4
DOIs
Publication statusPublished - 2010
Event2010 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2010 - Cergy-Pontoise, France
Duration: 7 Dec 201010 Dec 2010

Publication series

NameProceedings of the 2010 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2010

Conference

Conference2010 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2010
Country/TerritoryFrance
CityCergy-Pontoise
Period7/12/1010/12/10

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