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Title: Building collapse assessment in urban areas using texture information from postevent SAR data
Authors: Sun, W
Shi, L
Yang, J
Li, P
Keywords: Building collapse
Random forests (RFs)
Synthetic aperture radar (SAR)
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE journal of selected topics in applied earth observations and remote sensing, 2016, v. 9, no. 8, 7502166, p. 3792-3808 How to cite?
Journal: IEEE journal of selected topics in applied earth observations and remote sensing 
Abstract: A number of earthquakes have occurred in recent years, posing a challenge to Earth observation techniques. Owing to the all-weather response capability, synthetic aperture radar (SAR) has become a key tool for collapse interpretation. As the requirement for multitemporal data is usually not satisfied in practice, interpretation using only postevent SAR imagery is indispensable for emergency rescue. Despite being found that texture has a relationship with building damage, only a few texture measures have been adopted using simple threshold criteria. To fully explore the spatial contextual information in very high resolution (VHR) SAR images, there are two questions that should be discussed: 1) Which textural features are helpful for collapse assessment? 2) How do diverse imaging configurations influence the interpretation? In response, five texture descriptors are used for the stricken area texture extraction in this paper, and a random forests classifier is applied to identify the building collapse level. It was found that most of the gray-level histogram features perform quite well, and several other primitive features, such as the isolated bright points originating from scattered rubble, can also help to discriminate different collapse levels. Moreover, the experiments with data from the Yushu earthquake of April 14, 2010, indicated that spatial detail quality is the key for texture interpretation, and the span image can be considered as an appropriate choice when VHR PolSAR data are available. The optimal interpretation results (with an overall accuracy of 84.7%) were obtained using the 122 proposed measures in a VHR X-band image.
ISSN: 1939-1404
EISSN: 2151-1535
DOI: 10.1109/JSTARS.2016.2580610
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