ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-8, 43-50, 2016
https://doi.org/10.5194/isprs-annals-III-8-43-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
07 Jun 2016
AUTOMATIC BUILDING DAMAGE DETECTION METHOD USING HIGH-RESOLUTION REMOTE SENSING IMAGES AND 3D GIS MODEL
Jihui Tu1,2, Haigang Sui1, Wenqing Feng1, and Zhina Song1 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, 3D GIS Model, Level set Abstract. In this paper, a novel approach of building damaged detection is proposed using high resolution remote sensing images and 3D GIS-Model data. Traditional building damage detection method considers to detect damaged building due to earthquake, but little attention has been paid to analyze various building damaged types(e.g., trivial damaged, severely damaged and totally collapsed.) Therefore, we want to detect the different building damaged type using 2D and 3D feature of scenes because the real world we live in is a 3D space. The proposed method generalizes that the image geometric correction method firstly corrects the post-disasters remote sensing image using the 3D GIS model or RPC parameters, then detects the different building damaged types using the change of the height and area between the pre- and post-disasters and the texture feature of post-disasters. The results, evaluated on a selected study site of the Beichuan earthquake ruins, Sichuan, show that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and well suited for rapid damage assessment after natural disasters.
Conference paper (PDF, 1174 KB)


Citation: Tu, J., Sui, H., Feng, W., and Song, Z.: AUTOMATIC BUILDING DAMAGE DETECTION METHOD USING HIGH-RESOLUTION REMOTE SENSING IMAGES AND 3D GIS MODEL, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-8, 43-50, https://doi.org/10.5194/isprs-annals-III-8-43-2016, 2016.

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