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Assessing the Performance of Multi-Resolution Satellite SAR Images for Post-Earthquake Damage Detection and Mapping Aimed at Emergency Response Management
| Content Provider | MDPI |
|---|---|
| Author | Mazzanti, Paolo Scancella, Stefano Virelli, Maria Frittelli, Stefano Nocente, Valentina Lombardo, Federico |
| Copyright Year | 2022 |
| Description | The increasing availability of satellite Synthetic Aperture Radar (SAR) images is opening new opportunities for operational support to predictive maintenance and emergency actions. With the purpose of investigating the performances of SAR images characterized by different geometric resolutions for post-earthquake damage detection and mapping, we analyzed three SAR image datasets (Sentinel-1, COSMO-SkyMed Spotlight, and COSMO-SkyMed StripMap) available in Norcia (Central Italy) that were severely affected by a strong seismic sequence in 2016. By applying the amplitude and the coherent change detection processing tools, we compared pairs of images with equivalent features collected before and after the main shock on 30 October 2016 (at 06:40, UTC). Results were compared against each other and then measured against the findings of post-earthquake field surveys for damage assessment, performed by the Italian National Fire and Rescue Service (Corpo Nazionale dei Vigili del Fuoco—CNVVF). Thanks to the interesting and very rare opportunity to have pre-event COSMO-SkyMed Spotlight images, we determined that 1 × 1-m nominal geometric resolutions can provide very detailed single-building damage mapping, while COSMO-SkyMed StripMap HIMAGE images at 3 × 3-m resolutions return relatively good detections of damaged buildings; and, the Sentinel-1 images did not allow acquiring information on single buildings—they simply provided approximate identifications of the most severely damaged sectors. The main outcomes of the performance investigation we carried out in this work can be exploited considering the exponentially growing satellite market in terms of revisit time and image resolution. |
| Starting Page | 2210 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs14092210 |
| Journal | Remote Sensing |
| Issue Number | 9 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-05-05 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Synthetic Aperture Radar Earthquake Change Detection Natural Hazard Emergency Assessment Iris Software Disaster Monitoring |
| Content Type | Text |
| Resource Type | Article |
