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.
Highlights
A landslide is a geological occurrence that can cause severe loss of life, agricultural production, and the environment
The main objective of this paper is to devise a method for convenient and efficient building damage assessment based on an analysis of images of these structures in areas affected by landslides
We studied the spectral characteristics of landslides and proposed an sparse representation classification (SRC)-based approach for automatic landslide detection
Summary
A landslide is a geological occurrence that can cause severe loss of life, agricultural production, and the environment. Landslide disasters occur almost every year, and response time is a crucial factor in the effectiveness of postdisaster relief in such situations. In order to adequately provide emergency relief, decision makers and rescue organizations need a global perspective on the situation onsite.[1] With the development of satellite and airborne remote-sensing technologies, the outputs of very high-resolution (VHR) optical images and synthetic aperture radar (SAR) images have shown a marked improvement in quality.[2] These images have been widely used in a variety of fields that use remote sensing, one of which is image processing for postdisaster emergency response. Chesnel et al.[3] proposed a semiautomatic damage assessment method based on a pair of very high-spatial resolution images and some ancillary data. Their system assessed damage to buildings using the change detection
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