基于最优特征空间的震后损毁建筑物信息提取研究

Abstract

Abstract: This paper proposes an information extraction method which is based on traditional object-oriented method and the optimal feature space. In this method, images are segmented at an optimal scale by using ESP (estimate of scale parameter) tools. Then, distance matrix and minimum separation distance between various types of objects and ground are calculated, through the selected samples, so as to seek the optimal feature space. Finally, the post-earthquake images of damaged buildings are extracted according to the obtained optimal feature space. For the QuickBird image, the total precision of extraction is 83.1％ and the Kappa coefficient is 0.813. For the UAV(unmanned aerial vehicle) image, the total precision of extraction is 92.9％ and the Kappa coefficient is 0.940. Compared with the traditional object-oriented method, the precision and efficiency of the extraction method are greatly improved, which shows that this method has good popularization value in the information extraction of damaged buildings.

Key words: Optimal feature space, Damaged buildings, ESP

CLC Number:

TP79

CHEN Jin, CHEN Wen-kai, DOU Ai-xia, LI Wen, SUN Yan-ping. Information Extraction of Damaged Buildings after Earthquake Based on Optimal Feature Space[J]. EARTHQUAKE, 2019, 39(3): 71-83.

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Information Extraction of Damaged Buildings after Earthquake Based on Optimal Feature Space

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