A CMRF-Based Approach to Unsupervised Change Detection in Multitemporal Remote-Sensing Images

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 change detection of multitemporal remote-sensing differencing images confirm the effectiveness of proposed techniques.

关键词： CMRF(correlation MRF) Multi-temporal Remote-Sensing Images MAP(Maximum A Posterior) ICM(iteration condition model

作者: Yuan Qi Zhao Rongchun

作者单位: Department of Computer Science and Engineering,Northwestern Polytechnical University,Xian 710072

会议类型: 国际会议

会议名称: 第八届国际电子测量与仪器学术会议(Proceedings of 2007 8th International Conference on Electronic Measurement & Instruments)

会议地点: 西安

会议语种:英文

在线出版日期: 2007-08-16（万方平台首次上网日期，不代表论文的发表时间）