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Proceedings Paper

Damage assessment from remotely sensed images using PCA
Author(s): Masanobu Shinozuka; S. Ali Rejaie
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Paper Abstract

This study proposes a method to utilize remotely sensed pre- and post-disaster (bi-temporal) imagery data in order to detect the change specifically associated with structural and major regional damage caused by natural disasters such as a strong earthquake. The input is a pair of coregistered remotely sensed images of the same scene acquired at different times and the output is a binary image in which 'changed' pixels are separated from 'not-changed' ones. Correlation analysis generally fails to detect structural change, especially if images are acquired under different illumination conditions. In fact, automated detection in such a case becomes problematic since making distinction of change due to structural damage from that associated with the difference in the illumination condition is difficult. To overcome this problem, a method of principal component analysis (PCA) is employed. The approach produced promising results on the model images and currently under further study to be extended for near real-time damage assessment purposes.

Paper Details

Date Published: 30 July 2001
PDF: 10 pages
Proc. SPIE 4330, Smart Structures and Materials 2001: Smart Systems for Bridges, Structures, and Highways, (30 July 2001); doi: 10.1117/12.434106
Show Author Affiliations
Masanobu Shinozuka, Univ. of Southern California (United States)
S. Ali Rejaie, Univ. of Southern California (United States)


Published in SPIE Proceedings Vol. 4330:
Smart Structures and Materials 2001: Smart Systems for Bridges, Structures, and Highways
S.-C. Liu, Editor(s)

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