Ateş, Hasan Fehmiİmamoğlu, MüminKahraman, Fatih2017-03-132017-03-132016Kahraman, F., İmamoğlu, M. & Ateş, H. F. (2016). Battle damage assessment based on self-similarity and contextual modeling of buildings in dense urban areas. Paper presented at the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 5161-5164. doi:10.1109/IGARSS.2016.77303459781509033324978150903331797815090333312153-70032153-6996https://hdl.handle.net/11729/1198http://dx.doi.org/10.1109/IGARSS.2016.7730345Assessment 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.eninfo:eu-repo/semantics/closedAccessBattle Damage AssessmentBuilding Damage DetectionMarkov Random FieldRemote SensingSelf Similarity DescriptorBuildingsSatellitesContext modelingStripsSpatial resolutionObject detectionMilitary systemsBuilding contextual modelingSelf-similarity modelingTactical planningPost-war satellite imagesPre-war satellite imagesBuilding footprintsDetection accuracyGaza stripUNOSAT ground truth dataConference Object51615164WOS:0003881146050212-s2.0-85007439455Q3