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Object-level change detection based on full-scale image segmentation and its application to Wenchuan Earthquake

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Abstract

Aiming at object fragmentation and poor detection results caused by discontinuous segmentation scale in object-level change detection, a new object-level change detection method based on the full-scale object tree is presented in this paper. The core idea of this new algorithm is to establish the full-scale object tree based on convexity model theory and integrate full-scale image segmentation techniques and change detection into the whole process. Some Wenchuan Earthquake images are taken as an example to discuss the new method for earthquake damage detection and evaluation in urban area, landslide detection, and extraction of barrier lake boundary. The application shows that the new method is robust and it provides an advanced tool for the quantitative detection and evaluation of earthquake damage.

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Authors and Affiliations

  1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430072, China

    JianYa Gong, HaiGang Sui, KaiMin Sun, GuoRui Ma & JunYi Liu

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  1. JianYa Gong
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  2. HaiGang Sui
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  3. KaiMin Sun
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  4. GuoRui Ma
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  5. JunYi Liu
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Corresponding author

Correspondence to JianYa Gong.

Additional information

Supported by the National Basic Research Program of China (“973” Program) (Grant No. 2006CB701304) and the National Natural Scienc Foundation of China (Grant No. 60602013)

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Gong, J., Sui, H., Sun, K. et al. Object-level change detection based on full-scale image segmentation and its application to Wenchuan Earthquake. Sci. China Ser. E-Technol. Sci. 51 (Suppl 2), 110–122 (2008). https://doi.org/10.1007/s11431-008-6017-y

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  • DOI: https://doi.org/10.1007/s11431-008-6017-y

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