A multisensor approach for the 2016 Amatrice earthquake damage assessment

Vito Romaniello Vito Romaniello,Alessandro Piscini Alessandro Piscini,Christian Bignami Christian Bignami,Salvatore Stramondo Salvatore Stramondo,Roberta Anniballe Roberta Anniballe

doi:10.4401/ag-7185

Vito Romaniello Vito Romaniello, Alessandro Piscini Alessandro Piscini + Show 3 more

Open Access

https://doi.org/10.4401/ag-7185

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Abstract

This work proposes methodologies aimed at evaluating the damage occurred in the Amatrice town by using optical and Synthetic Aperture Radar (SAR) change features obtained from satellite images. The objective is to achieve a damage map employing the satellite change features in a classifier algorithm, namely the Features Stepwise Thresholding (FST) method. The main novelties of the proposed analysis concern the estimation of derived features at object scale and the exploitation of the unsupervised FST algorithm. A segmentation of the study area into several buildings blocks has been done by considering a set of polygons, over the Amatrice town, extracted from the open source Open Street Map (OSM) geo-database. The available satellite dataset is composed of several optical and SAR images, collected before and after the seismic event. Regarding the optical data, we selected the Normalised Difference Index (NDI), 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 (ICD) and the KLD parameter. The exploitation of these features in the FST algorithm permits to obtain a plausible damage map that is able to indicate the most affected areas.<em></em>

Highlights

An earthquake damage map, available right after a seismic event, can guide the rescue teams interventions towards the most affected areas
As far as the optical data is concerned, the most significative results are related to the Normalised Difference Index (NDI), KullbackLibler Divergence (KLD) (Kullback and Leiber (1951)), and Mutual Information (MI) indexes (Xie et al (2003))
The available dataset is made up of two Sentinel-2 optical images (1 pre- and 1 postseismic), three COSMO-Sky Synthetic Aperture Radar (SAR) images (2 pre- and 1 post-seismic) and a buildings footprint layer extracted by the Open Street Map service

Summary

INTRODUCTION

Available right after a seismic event (from few hours up to few days, depending on the satellite data availability), can guide the rescue teams interventions towards the most affected areas. (Matsuoka and Yamazaki (2004); Hoffman et al (2004); Yonezawa and Takeuchi (2001); Stramondo et al (2006); Chini et al (2013)) By exploiting both optical and radar sensors, and using change features achieved from the statistical analysis, a more accurate and reliable damage mapping can be obtained. As far as the optical data is concerned, the most significative results are related to the Normalised Difference Index (NDI), KullbackLibler Divergence (KLD) (Kullback and Leiber (1951)), and Mutual Information (MI) indexes (Xie et al (2003)) All these features show a good sensitivity to the collapse ratio. An unsupervised algorithm to estimate the damage level by satellite features, namely in the paper Features Stepwise Thresholding (FST), has been used (Roaniello et al (2016))

DATA AND METHODS

RESULTS

DISCUSSION