首页 | 本学科首页   官方微博 | 高级检索  
     

对马尔可夫随机场特征级图像融合的改进
引用本文:倪翠,关泽群,王斌,朱素娟.对马尔可夫随机场特征级图像融合的改进[J].计算机工程与应用,2011,47(32):211-214.
作者姓名:倪翠  关泽群  王斌  朱素娟
作者单位:同济大学测量与国土信息工程系,上海,200092
基金项目:国家985项目(No.0200144055)
摘    要:提出了一种基于MAP的Markov随机场的图像融合方法。将感兴趣区特征的均值与方差作为马尔可夫随机场的概率参数,选取合适的模型,根据优化算法快速求得MAP解,完成图像初始标记过程,根据最大后验概率模型,对图像进行特征层融合。通过两组遥感图像的实验,证明MAP-MRF模型在遥感图像特征层融合中,具有较目前常用方法更好的效果。

关 键 词:特征级图像融合  最大后验概率  马尔可夫随机场  迭代条件模型
修稿时间: 

Improvement to feature-level fusion of images based on MRF
NI Cui,GUAN Zequn,WANG Bin,ZHU Sujuan.Improvement to feature-level fusion of images based on MRF[J].Computer Engineering and Applications,2011,47(32):211-214.
Authors:NI Cui  GUAN Zequn  WANG Bin  ZHU Sujuan
Affiliation:NI Cui,GUAN Zequn,WANG Bin,ZHU Sujuan Department of Surveying and Geo-informatics Engineering,Tongji University,Shanghai 200092,China
Abstract:This paper proposes a method of Markov random field to classify the remote sensing images based on the maximum a posteriori model.According the extraction of characteristic,image fusion course can be carried on.This method makes the mean and variance of the information in region of interest to be the probability parameters.Proper model is selected to get the exact solution of MAP rapidly in the course of iterated conditional mode.The whole fusion course can be finished. By means of two experiments,the propo...
Keywords:feature-level fusion  Maximum A Posterior(iMAP)  Markov Random Field(MRF)  Iterated Conditional Mode(ICM)  
本文献已被 CNKI 维普 万方数据 等数据库收录!
点击此处可从《计算机工程与应用》浏览原始摘要信息
点击此处可从《计算机工程与应用》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号