Abstract
Abstract The damage of buildings is the major cause of casualties of from earthquakes. The traditional pixel-based earthquake damaged building detection method is prone to be affected by speckle noise. In this paper, an object-based change detection method is presented for the detection of earthquake damage using the synthetic aperture radar (SAR) data. The method is based on object-level texture features of SAR data. Firstly, the principal component analysis is used to transform the optimal texture features into a suitable feature space for extracting the key change. And then, the feature space is clustered by the watershed segmentation algorithm, which introduces the concept of object orientation and carries out the calculation of the difference map at the object level. Having training samples, the classification threshold values for different grade of earthquake damage can be trained, and the detection of damaged building is achieved. The proposed method could visualize the earthquake damage efficiently using the Advanced Land Observing Satellite-1 (ALOS-1) images. Its performance is evaluated in the town of jiegu, which was hit severely by the Yushu Earthquake. The cross-validation results shows that the overall accuracy is significantly higher than TDCD and IDCD.
Highlights
A rapid earthquake damage detection right after a seismic event can address first aid and relief towards the most affected areas
Compared with optical sensors,Synthetic aperture radar (SAR) is not affected by weather and lighting conditions that typically affect the observations in the optical spectral range, which has been widely used in building change detection to measure the extension of damage (XIONG et al 2012; AGHABABAEE et al 2013; HANCHICHAS et al 2014; MARINO et al 2014).Building damage assessment is usually regarded as a change detection problem (Gamba et al 2007; Jin et al 2009; Balz et al 2010), in which the mapping classes are correlated with the grade of damage suffered by the buildings
We present an object-level damage building detection method using texture features of pre- and post-earthquake SAR images based on watershed segmentation
Summary
Change detection (CD) using satellite remote-sensing observations has been an important technique for various applications such as land-cover/land-use change analyses (Nunes and Auge 1999; Shalaby and Tateishi 2007), assessment of deforestation (Hosonuma et al 2012; Römer et al 2012), damage assessment (Domenikiotis, Loukas and Dalezios 2003; Kamthonkiat et al 2011; Feng et al 2014), disaster monitoring (Lahousse, Chang and Lin 2011; Kaiser et al 2013), and other environmental changes Both optical and radar sensors can be exploited for change detection purposes. Compared with optical sensors ,Synthetic aperture radar (SAR) is not affected by weather and lighting conditions that typically affect the observations in the optical spectral range, which has been widely used in building change detection to measure the extension of damage (XIONG et al 2012; AGHABABAEE et al 2013; HANCHICHAS et al 2014; MARINO et al 2014).Building damage assessment is usually regarded as a change detection problem (Gamba et al 2007; Jin et al 2009; Balz et al 2010), in which the mapping classes are correlated with the grade of damage suffered by the buildings
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