This work proposes methodologies aimed at evaluating the sensitivity of optical and synthetic aperture radar (SAR) change features obtained from satellite images with respect to the damage grade due to an earthquake. The test case is the Mw 7.0 earthquake that hit Haiti on January 12, 2010, located 25 km west–south–west of the city of Port-au-Prince. The disastrous shock caused the collapse of a huge number of buildings and widespread damage. The objective is to investigate possible parameters that can affect the robustness and sensitivity of the proposed methods derived from the literature. It is worth noting how the proposed analysis concerns the estimation of derived features at object scale. For this purpose, a segmentation of the study area into several regions has been done by considering a set of polygons, over the city of Port-au-Prince, extracted from the open source open street map geo-database. The analysis of change detection indicators is based on ground truth information collected during a postearthquake survey and is available from a Joint Research Centre database. The resulting damage map is expressed in terms of collapse ratio, thus indicating the areas with a greater number of collapsed buildings. The available satellite dataset is composed of optical and SAR images, collected before and after the seismic event. In particular, we used two GeoEye-1 optical images (one preseismic and one postseismic) and three TerraSAR-X SAR images (two preseismic and one postseismic). Previous studies allowed us to identify some features having a good sensitivity with damage at the object scale. Regarding the optical data, we selected the normalized difference index and two quantities coming from the information theory, namely the Kullback–Libler divergence (KLD) and the mutual information (MI). In addition, for the SAR data, we picked out the intensity correlation difference and the KLD parameter. In order to analyze the capability of these parameters to correctly detect damaged areas, two different classifiers were used: the Naive Bayes and the support vector machine classifiers. The classification results demonstrate that the simultaneous use of several change features from Earth observations can improve the damage estimation at object scale.
Due to the improved spatial resolution, Earth observation (EO) data, either from Synthetic Aperture Radar (SAR) or optical sensor, provide the opportunity to assess earthquake damage of individual buildings. However, the operational use of EO data for earthquake damage mapping is basically limited to the visual inspection of Very High Resolution (VHR) optical imagery. In this work we investigate the feasibility of a damage assessment product at single building scale from a pair of VHR SAR images acquired before and after a seismic event. We perform the change analysis using the Kullbach-Leibler divergence and the intensity ratio and then we associate detected changes to a building map provided as GIS layer. Finally the expected SAR signature of a collapsed building is considered to identify severely damaged buildings. In order to test the proposed methodology we use Spotlight COSMO-SkyMed SAR imagery of L’Aquila (Italy) collected before and after the earthquake occurred on April 6, 2009. A macroseismic survey on the whole central area of L’Aquila city based on the European Macroseismic Scale 1998 is used to assess the capability of VHR SAR images to map damage.
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.