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Journal of Applied Remote Sensing • Open Access • new

Damage assessment framework for landslide disaster based on very high-resolution images
Author(s): Bo Sun; Qihua Xu; Jun He; Zhen Liu; Ying Wang; Fengxiang Ge

Paper Abstract

It is well known that rapid building damage assessment is necessary for postdisaster emergency relief and recovery. Based on an analysis of very high-resolution remote-sensing images, we propose an automatic building damage assessment framework for rainfall- or earthquake-induced landslide disasters. The framework consists of two parts that implement landslide detection and the damage classification of buildings, respectively. In this framework, an approach based on modified object-based sparse representation classification and morphological processing is used for automatic landslide detection. Moreover, we propose a building damage classification model, which is a classification strategy designed for affected buildings based on the spectral characteristics of the landslide disaster and the morphological characteristics of building damage. The effectiveness of the proposed framework was verified by applying it to remote-sensing images from Wenchuan County, China, in 2008, in the aftermath of an earthquake. It can be useful for decision makers, disaster management agencies, and scientific research organizations.

Paper Details

Date Published: 28 June 2016
PDF: 15 pages
J. Appl. Remote Sens. 10(2) 025027 doi: 10.1117/1.JRS.10.025027
Published in: Journal of Applied Remote Sensing Volume 10, Issue 2
Show Author Affiliations
Bo Sun, Beijing Normal Univ. (China)
Qihua Xu, Beijing Normal Univ. (China)
Northwest Normal Univ. (China)
Jun He, Beijing Normal Univ. (China)
Zhen Liu, Beijing Normal Univ. (China)
Ying Wang, Beijing Normal Univ. (China)
Fengxiang Ge, Beijing Normal Univ. (China)


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