Show simple item record

dc.contributor.authorAteş, Hasan Fehmien_US
dc.contributor.authorİmamoğlu, Müminen_US
dc.contributor.authorKahraman, Fatihen_US
dc.date.accessioned2017-03-13T11:49:05Z
dc.date.available2017-03-13T11:49:05Z
dc.date.issued2016
dc.identifier.citationKahraman, 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.2016.7730345en_US
dc.identifier.isbn9781509033324
dc.identifier.isbn9781509033317
dc.identifier.isbn9781509033331
dc.identifier.issn2153-7003
dc.identifier.issn2153-6996
dc.identifier.otherWOS:000388114605021
dc.identifier.urihttps://hdl.handle.net/11729/1198
dc.identifier.urihttp://dx.doi.org/10.1109/IGARSS.2016.7730345
dc.description.abstractAssessment 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.en_US
dc.description.sponsorshipThis research was supported in part by Republic of Turkey Prime Ministry Disaster and Emergency Management Presidency (AFAD) and TUBITAK BILGEM under Grants B740-G585000en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.isversionof10.1109/IGARSS.2016.7730345
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBattle Damage Assessmenten_US
dc.subjectBuilding Damage Detectionen_US
dc.subjectMarkov Random Fielden_US
dc.subjectRemote Sensingen_US
dc.subjectSelf Similarity Descriptoren_US
dc.subjectBuildingsen_US
dc.subjectSatellitesen_US
dc.subjectContext modelingen_US
dc.subjectStripsen_US
dc.subjectSpatial resolutionen_US
dc.subjectObject detectionen_US
dc.subjectMilitary systemsen_US
dc.subjectBuilding contextual modelingen_US
dc.subjectSelf-similarity modelingen_US
dc.subjectTactical planningen_US
dc.subjectPost-war satellite imagesen_US
dc.subjectPre-war satellite imagesen_US
dc.subjectBuilding footprintsen_US
dc.subjectDetection accuracyen_US
dc.subjectGaza stripen_US
dc.subjectUNOSAT ground truth dataen_US
dc.titleBattle damage assessment based on self-similarity and contextual modeling of buildings in dense urban areasen_US
dc.typeconferenceObjecten_US
dc.description.versionPublisher's Versionen_US
dc.relation.journal2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)en_US
dc.contributor.departmentIşık Üniversitesi, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.contributor.departmentIşık University, Faculty of Engineering, Department of Electrical-Electronics Engineeringen_US
dc.contributor.authorID0000-0002-6842-1528
dc.identifier.startpage5161
dc.identifier.endpage5164
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.contributor.institutionauthorAteş, Hasan Fehmien_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record