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Knowledge-Based Identification and Damage Detection of Bridges Spanning Water via High-Spatial-Resolution Optical Remotely Sensed Imagery

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Abstract

Bridges over water are important artificial objects that can be damaged by natural disasters. Accurate identification and damage detection of such bridges through the use of high-spatial-resolution optical remotely sensed imagery are important in emergency rescue and lifeline safety assessment. In this study, we detail a knowledge-based method of identification and damage detection of bridges spanning water using high-spatial-resolution optical remotely sensed imagery. Data on the body of water are extracted to define spatial extent and improve the timeliness of identification and damage detection, the threshold values of the rectangle degree and area are set to remove false bridge targets, and the damaged parts are detected according to the bridge’s rectangular characteristics and the relationship with the body of water. First, the characteristics, such as spectral, geometric, and textural, and spatial relationships of the bridge over water, are analyzed. Second, to limit the spatial extent of bridge identification and improve computational efficiency, data on the body of water are extracted. Third, the post-event bridge is identified from the viewpoint of bridge integrity based on shape and area parameters. Damage detection is then performed according to the bridge’s integrity. Finally, the results are evaluated for both non-positional and positional accuracy. Results of experiments carried out in Huiyang and Wenchuan, China, show that the proposed method, using high-spatial-resolution optical remotely sensed imagery, is effective for identification and damage detection of fallen and collapsed bridges spanning water. Therefore, the method is useful in updating the geographic database of bridges and assessing damage to them caused by natural disasters.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their constructive comments and suggestions. This work was supported by the National Natural Science Foundation of China (41701447); Training Program of Excellent Master Thesis of Zhejiang Ocean University; Open Foundation from Fishery Sciences in the First-Class Subjects of Zhejiang Ocean University; State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University (2018-KF-02). We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

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Authors and Affiliations

  1. Marine Science and Technology College, Zhejiang Ocean University, Zhoushan, 316022, Zhejiang, China

    Chao Chen, Jiaoqi Fu, Jiachen Hu & Biyun Guo

  2. Information Center of Ministry of Ecology and Environment, Beijing, 100083, China

    Nan Lu

  3. School of Port and Transportation Engineering, Zhejiang Ocean University, Zhoushan, 316022, Zhejiang, China

    Yanli Chu

  4. Taishan College of Science and Technology, Shandong University of Science and Technology, Tai’an, 271019, China

    Xin Zhao

  5. College of Geomatics, Shandong University of Science and Technology, Qingdao, 266590, China

    Xin Zhao

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  1. Chao Chen
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Correspondence to Nan Lu.

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Chen, C., Fu, J., Lu, N. et al. Knowledge-Based Identification and Damage Detection of Bridges Spanning Water via High-Spatial-Resolution Optical Remotely Sensed Imagery. J Indian Soc Remote Sens 47, 1999–2008 (2019). https://doi.org/10.1007/s12524-019-01036-z

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