基于广义高斯模型的SAR幅度图像震害检测

Abstract

Abstract: In this paper we use ALOS data before and after the 2008 Wenchuan earthquake to evaluate the seismic damages caused by the earthquake. Surface change caused by the earthquake has been automatically detected based on the generalized Gaussian model and KI optimal threshold value change detection method. Under the assumption that the changed pixels and unchanged pixels follow generalized Gaussian distribution , probability densities of the two classes of pixels are estimated. The KI threshold selection criterion is derived under the generalized Gaussian assumption for modeling the distributions of changed and unchanged classes. Applying this method to the city of Dujiangyan, some serious changed regions can be quickly identified. The results show that the changed map can be extracted based on the optimal threshold. SAR technique is an effective means to monitor natural disasters due to its all-weather characteristics.

Key words: SAR Data, Generalized Gaussian distribution, Earthquake damage assessment, KI threshold selection

CLC Number:

P315.9

LIU Yun-hua, YU Lu, SHAN Xin-jian. Earthquake Disaster Assessment Based on Generalized Gaussian Model Using SAR Amplitude Data[J]. EARTHQUAKE, 2013, 33(2): 29-36.

References

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Earthquake Disaster Assessment Based on Generalized Gaussian Model Using SAR Amplitude Data

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