Anniballe, Roberta

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.

The assessment of satellite image classifications is usually carried out using a test sample assumed as the ground truth, from which a confusion matrix is derived. There are cases where the reference data, even those coming from a ground survey, are affected by errors and do not represent a reliable truth. In the field of geophysical parameter retrieval, the triple collocation (TC) technique is applied for validating remotely sensed products when the source of test data (e.g., ground data) does not represent a reliable reference. TC is able to retrieve the error variances of three systems observing the same target parameter, assuming that their errors are independent. In this paper, we exploit the same idea to test the classification accuracy in cases where the ground truth is not available. We extend the TC approach to the classification problem for a general number of classes, but we solve it numerically for a two-class problem (i.e., collapsed and noncollapsed buildings). The specific case refers to the detection of L'Aquila 2009 earthquake damage from very high-resolution optical data. The image classification, performed by exploiting an object-based analysis, is compared with those from two different ground surveys carried out after the earthquake by different teams and with different purposes. This paper demonstrates the power of the TC approach for assessing the classification accuracy with no reliable ground truth available, and provides an insight into the problem of assessing damage, from satellite and on ground, in a very critical and unsafe situation, like the one occurring after an earthquake. Moreover, it was found that the remotely sensed product can have an order of accuracy comparable to that of at least one of the ground surveys.

Earth Observation (EO) data are used to map mostly affected urban areas after an earthquake generally exploiting change detection techniques applied at pixel scale. However, Civil Protection Services require damage assessment of each building according to a well-established scale to manage rescue operations and to estimate the economic losses. Considering the earthquake that hit L'Aquila city (Italy) on April 6, 2009, this work assess the feasibility of producing damage maps at the scale of single building from Very High Resolution (VHR) optical images collected before and after the seismic event. We considered the European Macroseismic Scale 1998 (EMS-98) and assessed the possibility to discriminate between collapsed or heavy damaged buildings (damage grade DG equal to 5 in the EMS-98 scale) and less damaged or undamaged buildings (DG < 5 in the EMS-98). The proposed approach relies on a pre-existing urban map to identify image objects corresponding to building footprints. The image analysis is carried out according to many different parameters with the objective of assessing their effectiveness in singling out changes associated to the building collapse. Features describing texture and colour changes, as well statistical similarity and correlation descriptors, such as the Kullbach Leibler Distance and the Mutual Information, were included in our analysis. Two supervised classification approaches, respectively, based on the use of the Bayesian Maximum A Posteriori (MAP) criterion and on Support Vector Machines (SVM), were compared. In our experiment, we considered the whole L'Aquila historical centre comparing classification results with the ground survey performed by the Istituto Nazionale di Geofisica e Vulcanologia (INGV). The work represents one of the first attempt to detect damage at the scale of single building, validated against an extensive ground survey. It addresses methodological aspects, highlighting the potential of textural features computed at object scale and SVMs, and discuss potential and limitations of EO in this field compared to ground surveys.

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.