|Paper title||Dual-polarimetric Sentinel-1 ascending-descending measurements to quantify damage levels due to the 2016 Central Italy earthquake|
|Form of presentation||Poster|
Earthquakes are tremendous natural disasters that cause casualties and damages. During a seismic event, a fast damage assessment is an important step for post-disaster emergency response to reduce the impact of the disaster.
Within this context, remote sensing plays an important role. The optical sensors data is one of the possible tools due to its simple interpretability. However, optical radiation is severely affected by cloud cover, solar illumination, and other adverse meteorological conditions that make sometimes difficult information extraction. In contrast, radar sensors ensure all-day and almost all-weather observations, together with a wide area coverage and the Synthetic Aperture Radar (SAR), due to its almost all-weather and all-day fine spatial resolution imaging capabilities, can be a very useful tool to observe earthquake damages.
SAR observation of damaged areas is not straightforward, and it is typically based on bi-temporal approaches that contrast features derived from SAR imagery collected before the earthquake with the peer ones evaluated after the earthquake . Recently, features evaluated from dual-polarimetric SAR measurements have been proven to be very effective and accurate to map earthquake-induced damages .
However, the urban area is an inherently complex environment that trigger artifacts in the SAR image plane due to foreshortening, shadowing, or layover . These issues have been shown to be mitigated when using SAR imagery collected under ascending and descending passes.
Within this context, in this study a quantitative analysis of earthquake-induced damages is performed using dual polarimetric (DP) SAR imagery collected under ascending and descending passes and by contrasting SAR-derived info with ground information. First, a change detection approach, based on the reflection symmetry, i.e., a symmetry property that holds when dealing with natural distributed scenarios and results in uncorrelated co- and cross-polarized channels, is used to detect the changes that occurred after the earthquake. Then, an unsupervised classifier based on Fuzzy c-means clustering is developed to associate changes in a proper class of damage. Finally, the ascending and descending damage maps are properly combined and contrasted with the ground truth obtained by in-situ measurements. Preliminary results, obtained processing a set of dual polarimetric (DP) SAR data collected at C-band from Sentinel-1 mission in the Central Italy area affected by the earthquake in 2016, show that the joint use of dataset collected in ascending and descending orbit allows improving the results in terms of overall accuracy.
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