A CMRF-based approach to unsupervised change detection in multitemporal remote-sensing images

被引：0
|
作者：
Yuan Qi [1 ]
Zhao Rongchun [1 ]
机构：
[1] Northwestern Polytech Univ, Dept Comp Sci & Engn, Xian 710072, Peoples R China
来源：
ICEMI 2007: PROCEEDINGS OF 2007 8TH INTERNATIONAL CONFERENCE ON ELECTRONIC MEASUREMENT & INSTRUMENTS, VOL II | 2007年
关键词：
CMRF(correlation MRF); multi-temporal remote-sensing images; MAP(Maximum A Posterior); ICM(iteration condition model;
D O I：
暂无
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Simple MRF model based method usually suffers from less inaccuracy because it assumes that each subimage used to estimate features is homogeneous. In this paper, an adaptive algorithm based on the fields correlation Markov random field(CMRF) model is proposed. The labeling is obtained through solving a MAP problem by ICM. Features of each pixel are calculated by using only the pixels currently labeled as the same pattern, while the new labeling is obtained by using the adapted feature. The satisfying experimental results in chan-e detection of multitemporal remote-sensing differencing images confirm the effectiveness of proposed techniques.
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页码：898 / 904
页数：7
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