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http://hdl.handle.net/2122/15013
Authors: | Ferrentino, Emanuele* Nunziata, Ferdinando* Bignami, Christian* Graziani, Laura* Maramai, Alessandra* Migliaccio, Maurizio* |
Title: | Multi-polarization C-band SAR imagery to quantify damage levels due to the Central Italy earthquake | Journal: | International Journal of Remote Sensing | Series/Report no.: | 5/42 (2021) | Publisher: | Taylor & Francis | Issue Date: | 11-May-2021 | DOI: | 10.1080/01431161.2021.1933247 | Keywords: | remote sensing earthquake damage SAR polarimetry |
Abstract: | This study analyzes the ability of polarimetric Synthetic Aperture Radar (PolSAR) measurements to quantify post-earthquake damages. To achieve this goal, a twofold task is addressed: on one side a processing chain, which exploits multi-polarization SAR features, and a decision-tree classifier is proposed to quantify the levels of damage in earthquake-affected urbanized areas using dual-polarimetric (DP) SAR imagery. On the other side, a new damage index is developed that allows a fair spatial intercomparison of building-by-building information, collected via ground surveys on the damaged areas, and SAR-derived damage maps. The proposed rationale is showcased using measurements related to the Central-Italy Earthquake occurred in 2016 where both Sentinel-1 DP imagery and ground-based information are available. Experimental results demonstrate the soundness of the proposed approach. The main outcomes can be summarized as follows: a) DP features perform better than single-polarization ones; b) DP features exhibit a larger sensitivity to lower damage grades if compared to the single polarization (SP) feature; c) the accuracy of the estimated damage levels depends on the requested granularity in the damage maps; d) the accuracy obtained using DP features spans from 52% up to 71% when five and two damage classes are considered, respectively. |
Appears in Collections: | Article published / in press |
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File | Description | Size | Format | Existing users please Login |
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Ferrentino_2021_IJRS.pdf | restricted paper | 3.49 MB | Adobe PDF | |
TRES-PAP-2021-0249.R1_Proof_hi.pdf | Open Access Accepted manuscript | 2.24 MB | Adobe PDF | View/Open |
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