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http://dx.doi.org/10.7848/ksgpc.2020.38.3.187

Detection of Collapse Buildings Using UAV and Bitemporal Satellite Imagery  

Jung, Sejung (Department of Geospatial Information, Kyungpook National University)
Lee, Kirim (Department of Geospatial Information, Kyungpook National University)
Yun, Yerin (School of Convergence & Fusion System Engineering, Kyungpook National University)
Lee, Won Hee (School of Convergence & Fusion System Engineering, Kyungpook National University)
Han, Youkyung (School of Convergence & Fusion System Engineering, Kyungpook National University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.38, no.3, 2020 , pp. 187-196 More about this Journal
Abstract
In this study, collapsed building detection using UAV (Unmanned Aerial Vehicle) and PlanetScope satellite images was carried out, suggesting the possibility of utilization of heterogeneous sensors in object detection located on the surface. To this end, the area where about 20 buildings collapsed due to forest fire damage was selected as study site. First of all, the feature information of objects such as ExG (Excess Green), GLCM (Gray-Level Co-Occurrence Matrix), and DSM (Digital Surface Model) were generated using high-resolution UAV images performed object-based segmentation to detect collapsed buildings. The features were then used to detect candidates for collapsed buildings. In this process, a result of the change detection using PlanetScope were used together to improve detection accuracy. More specifically, the changed pixels acquired by the bitemporal PlanetScope images were used as seed pixels to correct the misdetected and overdetected areas in the candidate group of collapsed buildings. The accuracy of the detection results of collapse buildings using only UAV image and the accuracy of collapse building detection result when UAV and PlanetScope images were used together were analyzed through the manually dizitized reference image. As a result, the results using only UAV image had 0.4867 F1-score, and the results using UAV and PlanetScope images together showed that the value improved to 0.8064 F1-score. Moreover, the Kappa coefficiant value was also dramatically improved from 0.3674 to 0.8225.
Keywords
Collapsed Buildings Detection; UAV; Satellite; Feature Information;
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Times Cited By KSCI : 2  (Citation Analysis)
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