Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7, 173-179, 2014
https://doi.org/10.5194/isprsarchives-XL-7-173-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
19 Sep 2014
A Novel 3D Building Damage Detection Method Using Multiple Overlapping UAV Images
H. Sui1, J. Tu1,2, Z. Song1, G. Chen1, and Q. Li1 1State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 430079 Wuhan, China
2Electronics & Information School of Yangtze University, Jingzhou , Hubei 434023, China
Keywords: Building Damage Detection, 3D Change Detection, 3D registration, UAV Images, SFM Abstract. In this paper, a novel approach is presented that applies multiple overlapping UAV imagesto building damage detection. Traditional building damage detection method focus on 2D changes detection (i.e., those only in image appearance), whereas the 2D information delivered by the images is often not sufficient and accurate when dealing with building damage detection. Therefore the detection of building damage in 3D feature of scenes is desired. The key idea of 3D building damage detection is the 3D Change Detection using 3D point cloud obtained from aerial images through Structure from motion (SFM) techniques. The approach of building damage detection discussed in this paper not only uses the height changes of 3D feature of scene but also utilizes the image's shape and texture feature. Therefore, this method fully combines the 2D and 3D information of the real world to detect the building damage. The results, tested through field study, demonstrate that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and suited well for rapid damage assessment after natural disasters.
Conference paper (PDF, 1310 KB)


Citation: Sui, H., Tu, J., Song, Z., Chen, G., and Li, Q.: A Novel 3D Building Damage Detection Method Using Multiple Overlapping UAV Images, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7, 173-179, https://doi.org/10.5194/isprsarchives-XL-7-173-2014, 2014.

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