Automatic three-dimensional features extraction: The case study of L'Aquila for collapse identification after April 06, 2009 earthquake

This paper illustrates an innovative methodology for post-earthquake collapsed building recognition, based on satellite-image classification methodologies and height variation information. Together, the techniques create a robust classification that seems to yield good results in this application field. In the first part of this study, two different feature extraction methodologies were compared, based respectively on pixel-based and object-oriented approaches. Then the classification results of the most accurate classification methodology, obtained on an eight band WorldView-2 monoscopic image, were completed with height variation information before and after the event. The height difference is calculated, comparing a photogrammetric DSM, obtained using a photogrammetric rigorous orbital model on some EROS-B 0.7 metre across-track stereopairs with a 'roof model' before the earthquake

DSMs extraction methodologies from EROS-B "pseudo-stereopairs", PRISM stereopairs in coastal and post-seismic areas

The aim of this paper is to illustrate a series of experimentations conducted on the possibility of extracting digital surface models (DSM) from stereoscopic images and from non stereoscopic pair of images that have a relative geometry similar to stereoscopic. The research is focused on imagery acquired from satellite platforms little or no investigated for this specific application field, such as ALOS-PRISM and EROS-B. Images acquired by both satellites on areas morphologically different between them, both in the Abruzzo region (Italy); some images represents a coastal area that spans from the city of Pescara to the city of Ortona, other represents the area of l'Aquila, whose history was marked recently by a series of earthquakes that caused serious damages also because the city is situated partially on an ancient lake-bed that amplifies seismic activity. © 2013 Springer-Verlag Berlin Heidelberg