Building damage estimation model using terrasar-X observing the 2010 Haiti earthquake

Masashi Matsuoka, Hiroyuki Miura, Shunichi Koshimura, Yoshihisa Maruyama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the aim of developing a model for estimating building damage from high-resolution synthetic aperture radar (SAR) data at X-band, which is appropriate for Haiti, we propose a regression discriminant function based on damage interpretation dataset in Port-au-Prince, which was seriously affected by the 2010 Haiti earthquake. The function can discriminate damage ranks corresponding to the severe damage ratio of buildings using TerraSAR-X imagery of the disaster area before and after the earthquake. By calculating the difference in and correlation of backscattering coefficient, which were explanatory variables of the regression discriminant function, a normalized likelihood function for the severe damage ratio was developed based on discriminant scores of the regression discriminant function.

Original languageEnglish
Title of host publication34th Asian Conference on Remote Sensing 2013, ACRS 2013
PublisherAsian Association on Remote Sensing
Pages3646-3652
Number of pages7
ISBN (Print)9781629939100
Publication statusPublished - 2013 Jan 1
Event34th Asian Conference on Remote Sensing 2013, ACRS 2013 - Bali, Indonesia
Duration: 2013 Oct 202013 Oct 24

Publication series

Name34th Asian Conference on Remote Sensing 2013, ACRS 2013
Volume4

Other

Other34th Asian Conference on Remote Sensing 2013, ACRS 2013
CountryIndonesia
CityBali
Period13/10/2013/10/24

Keywords

  • Backscattering coefficient
  • Building damage ratio
  • Haiti
  • Likelihood function
  • TerraSAR-X image

ASJC Scopus subject areas

  • Computer Networks and Communications

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  • Cite this

    Matsuoka, M., Miura, H., Koshimura, S., & Maruyama, Y. (2013). Building damage estimation model using terrasar-X observing the 2010 Haiti earthquake. In 34th Asian Conference on Remote Sensing 2013, ACRS 2013 (pp. 3646-3652). (34th Asian Conference on Remote Sensing 2013, ACRS 2013; Vol. 4). Asian Association on Remote Sensing.