ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume IV-2/W5
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W5, 29–36, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W5-29-2019
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W5, 29–36, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W5-29-2019

  29 May 2019

29 May 2019

DAMAGE DETECTION ON BUILDING FAÇADES USING MULTI-TEMPORAL AERIAL OBLIQUE IMAGERY

D. Duarte, F. Nex, N. Kerle, and G. Vosselman D. Duarte et al.
  • Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands

Keywords: Convolutional Neural Networks, Change Detection, Manned-platforms, Remote Sensing, Deep Learning

Abstract. Over the past decades, a special interest has been given to remote-sensing imagery to automate the detection of damaged buildings. Given the large areas it may cover and the possibility of automation of the damage detection process, when comparing with lengthy and costly ground observations. Currently, most image-based damage detection approaches rely on Convolutional Neural Networks (CNN). These are used to determine if a given image patch shows damage or not in a binary classification approach. However, such approaches are often trained using image samples containing only debris and rubble piles. Since such approaches often aim at detecting partial or totally collapsed buildings from remote-sensing imagery. Hence, such approaches might not be applicable when the aim is to detect façade damages. This is due to the fact that façade damages also include spalling, cracks and other small signs of damage. Only a few studies focus their damage analysis on the façade and a multi-temporal approach is still missing. In this paper, a multi-temporal approach specifically designed for the image classification of façade damages is presented. To this end, three multi-temporal approaches are compared with two mono-temporal approaches. Regarding the multi-temporal approaches the objective is to understand the optimal fusion between the two imagery epochs within a CNN. The results show that the multi-temporal approaches outperform the mono-temporal ones by up to 22% in accuracy.