Abstract
The detection of damaged building regions is crucial to emergency response actions and rescue work after a disaster. Change detection methods using multi-temporal remote sensing images are widely used for this purpose. Differing from traditional methods based on change detection for damaged building regions, semantic scene change can provide a new point of view since it can indicate the land-use variation at the semantic level. In this paper, a novel method is proposed for detecting damaged building regions based on semantic scene change in a visual Bag-of-Words model. Pre- and post-disaster scene change in building regions are represented by a uniform visual codebook frequency. The scene change of damaged and non-damaged building regions is discriminated using the Support Vector Machine (SVM) classifier. An evaluation of experimental results, for a selected study site of the Longtou hill town of Yunnan, China, which was heavily damaged in the Ludian earthquake of 14 March 2013, shows that this method is feasible and effective for detecting damaged building regions. For the experiments, WorldView-2 optical imagery and aerial imagery is used.
Highlights
Natural disasters such as earthquakes can take thousands of human lives, cause extensive destruction to infrastructure, flatten buildings, and dramatically change the land surface
We proposed a quick and accurate approach based on semantic scene change for damaged building regions detection
Scene changes between the pre-disaster satellite image and post-disaster aerial images should be co-registered, and the spatial resolution of pre-disaster image is 0.5 m, while the spatial resolution of post-disaster image is 0.2 m
Summary
Natural disasters such as earthquakes can take thousands of human lives, cause extensive destruction to infrastructure, flatten buildings, and dramatically change the land surface. High-resolution aerial images can be obtained in a much more controlled fashion, both in terms of time and flight planning and at much higher geometric, spectral, and radiometric resolution This is more suitable for fast and reliable post-disaster damage assessment due to accessibility and rapid acquisition [1]. The approach uses feature extraction to detect the building damage supported by auxiliary pre-earthquake GIS vector data. This method is more suitable for detecting the extent of building damage in real time. Due to the abundant and detailed spatial information provided by high-resolution imagery, a great deal of research has focused on detecting the detailed damage information on individual buildings for later reconstruction in disaster areas This increases computation cost since various features of buildings are extracted such as height, area, and texture. Experimental results show the proposed method is a stable and effective way to detect damaged building regions
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