Unsupervised Change Detection in Multitemporal SAR Images Using MRF Models

被引：6
|
作者：
Jiang Liming [1 ]
Liao Mingsheng [1 ]
Zhang Lu [1 ]
Lin Hui [1 ]
机构：
[1] Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Shatin, Hong Kong, Peoples R China
来源：
GEO-SPATIAL INFORMATION SCIENCE | 2007年 / 10卷 / 02期
关键词：
change detection; multitemporal SAR image; Markov random field; EM algorithm;
D O I：
10.1007/s11806-007-0051-y
中图分类号：
TP7 [遥感技术];
学科分类号：
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要：
An unsupervised change-detection method that considers the spatial contextual information in a log-ratio difference image generated from multitemporal SAR images is proposed. A Markov random filed (MRF) model is particularly employed to exploit statistical spatial correlation of intensity levels among neighboring pixels. Under the assumption of the independency of pixels and mixed Gaussian distribution in the log-ratio difference image, a stochastic and iterative EM-MPM change-detection algorithm based on an MRF model is developed. The EM-MPM algorithm is based on a maximiser of posterior marginals (MPM) algorithm for image segmentation and an expectation-maximum (EM) algorithm for parameter estimation in a completely automatic way. The experiment results obtained on multitemporal ERS-2 SAR images show the effectiveness of the proposed method.
引用
收藏
页码：111 / 116
页数：6
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