Damage assessment framework for landslide disaster based on very high-resolution images

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. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License.