Ms. Roberta Anniballe Profile

Roberta Anniballe

at Sapienza Univ di Roma

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Publications (3)

SPIE Journal Paper | 30 March 2017 Open Access

Earthquake damage mapping by using remotely sensed data: the Haiti case study

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

JARS, Vol. 11, Issue 01, 016042, (March 2017) https://doi.org/10.1117/12.10.1117/1.JRS.11.016042

KEYWORDS: Synthetic aperture radar, Earthquakes, Chromium, Satellites, Buildings, Associative arrays, Satellite imaging, Statistical analysis, Algorithm development, Earth observing sensors

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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.

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Proceedings Article | 23 October 2015 Paper

Detecting earthquake damage in urban area: application to COSMO-SkyMed imagery of L’Aquila earthquake

R. Anniballe, M. Chini, N. Pierdicca, C. Bignami, S. Stramondo, F. Noto, T. Scalia, A. Martinelli, A. Mannella

Proceedings Volume 9642, 96420C (2015) https://doi.org/10.1117/12.2195723

KEYWORDS: Earthquakes, Synthetic aperture radar, Buildings, Backscatter, Image segmentation, Damage detection, Image resolution, Radar, Remote sensing, Geographic information systems, Spatial resolution, Associative arrays, Optical inspection

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Proceedings Article | 21 October 2014 Paper

Detecting sparse earthquake damages in high density urban settlements by VHR SAR data

R. Anniballe, C. Bignami, M. Chini, N. Pierdicca, S. Stramondo

Proceedings Volume 9243, 924307 (2014) https://doi.org/10.1117/12.2068738

KEYWORDS: Synthetic aperture radar, Buildings, Earthquakes, Backscatter, RGB color model, Satellites, Interferometry, Visualization, Composites, Sensors

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