Battle damage assessment based on self-similarity and contextual modeling of buildings in dense urban areas
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Dosyalar
Tarih
2016
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Yayıncı
IEEE
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Battle Damage Assessment, Building Damage Detection, Markov Random Field, Remote Sensing, Self Similarity Descriptor, Buildings, Satellites, Context modeling, Strips, Spatial resolution, Object detection, Military systems, Building contextual modeling, Self-similarity modeling, Tactical planning, Post-war satellite images, Pre-war satellite images, Building footprints, Detection accuracy, Gaza strip, UNOSAT ground truth data
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Künye
Kahraman, 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.2016.7730345
