Nowadays, space-borne Synthetic Aperture Radar (SAR) sensors, can achieve spatial resolutions in the order of 1 m.
However, the exploitation of SAR at very high resolution (VHR) for detecting sparse and isolated damages in urban
areas, caused by earthquakes, is still a challenging task. Within urban settlements, the scattering mechanisms are
extremely complex and simple change detection analyses or classification procedures can hardly be performed. In this
work the 2009, L’Aquila (Italy), earthquake has been considered as case study. Despite about 300 people were killed by
the earthquake, few buildings were completely collapsed, and many others were heavily/partially damaged, resulting in a
quite sparse damage distribution. We have visually analyzed pairs of VHR SAR data acquired by COSMO-SkyMed
satellites, in SPOTLIGHT mode, before and after the earthquake. Such analyses were performed to understand the SAR
response of damaged structures surrounded by unaffected buildings, with the aim to identify possible strategies to map
the damaged buildings by using an automatic classification procedure. The preliminary analyses based on RGB images,
generated by combining pre- and post-event backscattering images, allowed us to figure out how the completely
collapsed and the partially damaged buildings are characterized in the SAR response. These outcomes have been taken
into account to set up a decision tree algorithm (DTA). Decision rules and related thresholds were identified by
statistically analyzing the values of backscattering and derived features. This study point out that many pieces of
information and discrimination rules must be exploited to obtain reliable results when dealing with non-extensive and
sparse damage within a dense urban settlement.