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Yayın Disaster damage assessment of buildings using adaptive self-similarity descriptor(2016-08) Kahraman, Fatih; İmamoğlu, Mümin; Ateş, Hasan FehmiAssessment of damage caused by a disaster is significant for coordinating emergency response teams and planning emergency aid. In this letter, a robust method for rapid building damage assessment is proposed using pre- and postevent EO images and building footprints. The method uses a local self-similarity descriptor (SSD) for change detection in buildings, which is shown to be robust against variations in global illumination and small local deformations. The use of building footprints helps reduce the false alarms due to changes in nonbuilding areas. Footprint is also used to differentiate small and large buildings, extract the boundary region of a building, and adapt the descriptor computation accordingly. It is shown that the adaptive SSD provides a more accurate measure of local damage on the building. The 2010 Haiti Earthquake and Typhoon Haiyan 2013 Philippines are analyzed with the proposed method, and 75/82% true positive rate and 25/15% false positive rate are obtained for detection of collapsed buildings with respect to the ground truth data of UNITAR/UNOSAT and HOT.Yayın Co-registration of surfaces by 3D least squares matching(Amer Soc Photogrammetry, 2010-03) Akça, Mehmet DevrimA method for the automatic co-registration of 3D surfaces is presented. Die method utilizes the mathematical model of Least Squares 2D image matching and extends it for solving the 3D surface matching problem The transformation parameters of the search surfaces are estimated with respect to a template surface. The solution is achieved when the sum of the squares of the 3D Spatial (Euclidean) distances between the surfaces are minimized. The parameter estimation is achieved using the Generalized Gauss-Markov model. Execution level implementation details are given. Apart from the co-registration of the point clouds generated from spacaborne airborne and terrestinal sensors and techniques. the proposed method is also useful for change detection, 3D comparison, and quality assessment tasks Experiments, terrain data examples show file capabilities of the method.Yayın Assessment and enhancement of SAR noncoherent change detection of sea-surface oil spills(IEEE, 2018-01) Bayındır, Cihan; Frost, J. David; Barnes, Christopher F.Oil spills are one of the most dangerous catastrophes that threaten the oceans. Therefore, detecting and monitoring oil spills by means of remote sensing techniques that provide large-scale assessments is of critical importance to predict, prevent, and clean oil contamination. In this study, the detection of an oil spill using synthetic aperture radar (SAR) imagery is considered. Detection of the oil spill is performed using change detection algorithms between imagery acquired at different times. The specific algorithms used are the correlation coefficient change statistic and the intensity ratio change statistic algorithms. These algorithms and the probabilistic selection of threshold criteria are reviewed and discussed. A recently offered change detection method that depends on generating change maps of two images in a temporal sequence is used. An initial change map is obtained by cumulatively adding sequences in such a manner that common change areas are excluded and uncommon change areas are included. A final change map is obtained by comparing the first and the last images in the temporal sequence. This method requires at least three images to be employed and can be generalized to longer temporal image sequences. The purpose of this approach is to provide a double-check mechanism to the conventional approach and, thus, reduce the probability of false alarm while enhancing change detection. The algorithms are tested on 2010 Gulf of Mexico oil spill imagery. It is shown that the intensity ratio change statistic is a better tool for identification of the changes due to the oil spill compared to the correlation coefficient change statistic. It is also shown that the proposed method can reduce the probability of false alarm.Yayın Disaster damage assessment for buildings using self-similarity descriptor(Institute of Electrical and Electronics Engineers Inc, 2015) Kahraman, Fatih; İmamoğlu, Mümin; Ateş, Hasan FehmiAssessment of damage caused by an earthquake is significant for coordinating emergency response teams and planning emergency aid. In this study, a robust method is proposed for detecting damaged buildings using pre- and post-event satellite images and building footprints. The method uses local self-similarity descriptor for change detection in buildings, which is shown to be robust against variations in illumination and small local deformations. The use of building footprints helps reduce the false alarms due to changes in non-building areas. The 2010 Haiti earthquake is analyzed with the suggested method and 72% true positive rate and 29% false positive rate are obtained for detection of collapsed buildings with respect to the ground truth data of UNITAR/UNOSAT.
