BATTLE DAMAGE ASSESSMENT BASED ON SELF-SIMILARITY AND CONTEXTUAL MODELING OF BUILDINGS IN DENSE URBAN AREAS

KAHRAMAN F., Imamoglu M., Ates H. F.

36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 10 - 15 July 2016, pp.5161-5164, (Full Text) identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/igarss.2016.7730345
  • City: Beijing
  • Country: China
  • Page Numbers: pp.5161-5164
  • Dokuz Eylül University Affiliated: No

Abstract

Assessment of battle damages is significant both for tactical planning and for after-war relief efforts. In this study damaged buildings are detected using self-similarity descriptor in pre-and post-war satellite images. Detection accuracy is improved by the use of a contextual model that describes the building neighborhoods. Building footprints are utilized for accurate assessment of building-level changes and for the formation of neighborhood context. The Gaza Strip after 2014 Israel-Palestine conflict is analyzed with the suggested method and 84% true positive rate and 19% false positive rate are obtained on the average for detection of damaged buildings with respect to the ground truth data of UNOSAT.