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 almost error free. In the first part of this study, two different features extraction methodologies were compared. Then, the classification results of the most accurate classification methodology were completed and confirmed with information from photogrammetric DSM compared with vectorial DSM of the pre-earthquake altimetry of the buildings. The different types of classification were performed on WorldView-2 monoscopic image of L'Aquila City; for DSM extraction, an Eros-B across-track stereopair was used.
Automatic three dimensional features extraction: The case study of L'Aquila for collapses identification after April, 06 2009 earthquake
BRIGANTE, RAFFAELLA;RADICIONI, Fabio
2014
Abstract
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 almost error free. In the first part of this study, two different features extraction methodologies were compared. Then, the classification results of the most accurate classification methodology were completed and confirmed with information from photogrammetric DSM compared with vectorial DSM of the pre-earthquake altimetry of the buildings. The different types of classification were performed on WorldView-2 monoscopic image of L'Aquila City; for DSM extraction, an Eros-B across-track stereopair was used.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
