Graph Cut Energy Driven Earthquake-damaged Building Detection from High-resolution Remote Sensing Images

doi:10.11947/j.AGCS.2017.20170035

Abstract

Abstract: In order to make full use of the detail information provided by high-resolution remote sensing images to improve the detection accuracy of damaged buildings during earthquake,combining the shape, edge and corner characteristics of buildings, a novel method based on graph cut frame for damaged building detection is proposed in this paper. Firstly, the local image containing single building used to model the energy function is constructed by digital line graphic data. And the bound terms of the energy function are defined by the location, shape, edge and corner of buildings, respectively. Then, the energy function is minimized through max-flow/min-cut method, and the similarity of the buildings in the pre-and post-event images is measured by the minimum cut energy. Finally, the EM algorithm is exploited to select the classification threshold value of the minimum cut energy, and post-processing is performed according to the misclassification rate estimation to obtain the final detection result. Images taken in Ishinomaki before and after the 2011 off the Pacific coast of Tohoku Earthquake are used in this paper. The experimental results show that the proposed method can effectively detect the damaged buildings.

Key words: damage detection, building, graph cut, high-resolution remote sensing image, object-oriented

CLC Number:

P237

LIU Ying, TAO Chao, YAN Pei, ZOU Zhengrong. Graph Cut Energy Driven Earthquake-damaged Building Detection from High-resolution Remote Sensing Images[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(7): 910-917.

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