A Novel Approach Combining KI Criterion and Inverse Gaussian Model to Unsupervised Change Detection in SAR Images

被引：0
|
作者：
Zhuang H. [1 ]
Deng K. [1 ]
Yu M. [1 ]
Fan H. [1 ,2 ]
机构：
[1] Jiangsu Key Laboratory of Resources and Environmental Information Engineering, China University of Mining and Technology, Xuzhou
[2] State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu
来源：
Deng, Kazhong (kzdeng@cumt.edu.cn) | 2018年 / Editorial Board of Medical Journal of Wuhan University卷 / 43期
基金：
中国国家自然科学基金;
关键词：
Bayes decision theorem; Change detection; Inverse Gaussian model (IGM); Kittler-Illingworth criterion; Synthetic aperture radar (SAR); Threshold selection;
D O I：
10.13203/j.whugis20160079
中图分类号：
学科分类号：
摘要：
In this context, a novel approach combining inverse Gaussian model (IGM) and the Kittler-Illingworth (KI) criterion has been proposed to carry out tunsupervised change detection in synthetic aperture radar (SAR) images. The minimum error threshold could be computed by exploiting the Bayes decision theory under the assumption that hybrid IGM could describe the distribution of the changed and unchanged class in difference image. Experiments carried out on two sets of multi-temporal SAR images indicate that the proposed approach can effectively estimate the probability density function of the unchanged and changed classes in the difference image and acquire a reasonable threshold for yielding a better change map from the difference image. © 2018, Research and Development Office of Wuhan University. All right reserved.
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收藏
页码：282 / 288
页数：6
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