基于广义高斯模型的SAR幅度图像震害检测

地震 ›› 2013, Vol. 33 ›› Issue (2): 29-36.

基于广义高斯模型的SAR幅度图像震害检测

刘云华, 庾露, 单新建

中国地震局地质研究所, 地震动力学国家重点实验室, 北京 100029

收稿日期:2012-12-30 出版日期:2013-04-30 发布日期:2020-09-27
作者简介:刘云华(1977-), 男, 河南南阳人, 助理研究员, 主要从事RS、 GIS技术在地震中的应用等研究。
基金资助:
地震行业科研专项(201108010)资助

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

LIU Yun-hua, YU Lu, SHAN Xin-jian

State Key Laboratory of Earthquake Dynamics, Institute of Geology, CEA , Beijing 100029, China

Received:2012-12-30 Online:2013-04-30 Published:2020-09-27

摘要/Abstract

摘要： 本文以2008年5月12日汶川地震前后的星载ALOS 合成孔径雷达图像作为数据源, 利用广义高斯分布模型作为差异图像中变化类和未变化类的分布模型, 在假设变化和未变化像元服从广义高斯分布的条件下, 估计变化和未变化像元的概率密度参数, 采用KI算法计算最佳分割阈值并提取变化区域。以都江堰地区为例, 自动检测这次地震导致的地表变化, 快速圈定一些变化差异较大的区域。研究表明, 该变化检测算法能较准确地描述差异影像的分布, 能够在地震灾害提取中发挥作用。

关键词: SAR图像, 广义高斯分布, 震害检测, KI阈值选取

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

中图分类号:

P315.9

刘云华, 庾露, 单新建. 基于广义高斯模型的SAR幅度图像震害检测[J]. 地震, 2013, 33(2): 29-36.

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.

参考文献

[1] Bouyahia Z, Benyoussef L, Derrode S. Change detection in synthetic aperture radar images with a sliding hidden Markov chain model[J]. Journal of Applied Remote Sensing, 2008, 2(1): 023526.
[2] Bujor F, Trouve E, Valet L, et al. Application of log-cumulants to the detection of spatiotemporal discontinuities in multitemporal SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(10): 2073-2084.
[3] Bazi Y. Change detection in multitemporal SAR images based on generalized Gaussian distribution and EM algorithm[C]. Proceedings of SPIE, 2004, 573: 364-375.
[4] Moser G, Serpico S B. Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(10): 2972-2982.
[5] Matsuoka M, Yamazaki F. Characteristics of satellite images of damaged areas due to the 1995 kobe earthquake[C]. in 2th Conference on the Applications of Remote Sensing and GIS for Disaster Management, 1999. 2-9.
[6] Aoki H, Matsuoka M, Yamazaki F. Characteristics of Satellite SAR Images in the Damaged Areas Due to the Hyogoken-Nanbu Earthquake[C]. in Proceedings of the 19th Asian Conference on Remote Sensing, 1998. 1-6.
[7] Matsuoka M, Yamazaki F. Interferometric characterization of areas damaged by the 1995 kobe earthquake using satellite sar images[C]. in Proceedings of the 12th World Conference on Earthquake Engineering, 2000. 1-8.
[8] Yakoub Bazi, Lorenzo Bruzzone, Farid Melgani. An Approach to Unsupervised Change Detection in Multitemporal SAR Images Based on the Generalized Gaussian Distribution[J]. IEEE Transactions On Geoscience And Remote Sensing.2005, 43(4): 874-887.
[9] Radke R J, Andra S, Al-kofahi O, et al. Image Change Detection Algorithms: A Systematic Survey[J]. Sensing And Imaging, 2004, 1-32.
[10] Rignot E J M, Zyl J J V. Change Detection Techniques for ERS-1 SAR Data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1993, 31(4): 896-906.
[11] 郭华东. 雷达对地观测理论与应用(第一版)[M]. 北京: 科学出版社, 2000.
[12] Bazi Y, Bruzzone L, Melgani F. An approach to unsupervised change detection in multitemporal SAR images based on the generalized Gaussian distribution[J]. IEEE International Geoscience and Remote, 2004, 1(C): 1402-1405.
[13] Lombardo P, Pellizzeri T M. Maximum likelihood signal processing techniques to detect a step pattern of change in multitemporal SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(4): 853-870.
[14] Zoltowski M D, Processing A, Haykin S. Generalized Gaussian Modeling of Correlated Signal Sources[J]. Ieee Transactions On Signal Processing, 1999, 47(1): 217-219.
[15] Kittler J, Illingworth J. Minimum error thresholding[J]. Pattern recognition, 1986, 19(1): 41-47.