Abstract
The damage of buildings and manmade structures, where most of human activities occur, is the major cause of casualties of from earthquakes. In this paper, an improved technique, Earthquake Damage Visualization (EDV) is presented for the rapid detection of earthquake damage using the Synthetic Aperture Radar (SAR) data. The EDV is based on the pre-seismic and co-seismic coherence change method. The normalized difference between the pre-seismic and co-seismic coherences, and vice versa, are used to calculate the forward (from pre-seismic to co-seismic) and backward (from co-seismic to pre-seismic) change parameters, respectively. The backward change parameter is added to visualize the retrospective changes caused by factors other than the earthquake. The third change-free parameter uses the average values of the pre-seismic and co-seismic coherence maps. These three change parameters were ultimately merged into the EDV as an RGB (Red, Green, and Blue) composite imagery. The EDV could visualize the earthquake damage efficiently using Horizontal transmit and Horizontal receive (HH), and Horizontal transmit and Vertical receive (HV) polarizations data from the Advanced Land Observing Satellite-2 (ALOS-2). Its performance was evaluated in the Kathmandu Valley, which was hit severely by the 2015 Nepal Earthquake. The cross-validation results showed that the EDV is more sensitive to the damaged buildings than the existing method. The EDV could be used for building damage detection in other earthquakes as well.
Highlights
Earthquakes are one of the most catastrophic natural disasters
This paper presents an improved technique called the Earthquake Damage Visualization (EDV)
The EDV was proposed in the research for visualizing the earthquake-induced building damage
Summary
Earthquakes are one of the most catastrophic natural disasters. More than 23 million deaths have been caused by earthquakes during the period of 1902–2011 alone globally Major techniques used for retrieving the earthquake-induced building damage information are object based classification [28,29,30], template matching and pattern recognition [31], and supervised classification [32,33,34]. Airborne imagery is another source of building damage information [35,36,37,38,39,40]. The performance of the EDV technique is demonstrated in the case of the 2015 Nepal Earthquake using the ALOS-2 SAR data and compared it to the existing NDPM method
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