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

Content Provider	IEEE Xplore Digital Library
Author	Yuan Qi Zhao Rongchun
Copyright Year	2007
Description	Author affiliation: Northwestern Polytech. Univ., Xi'an (Yuan Qi; Zhao Rongchun)
Abstract	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.
File Size	632897
File Format	PDF
ISBN	9781424411351
DOI	10.1109/ICEMI.2007.4350825
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Parameter estimation CMRF(correlation MRF) Instruments Image processing MAP(Maximum A Posterior) Remote sensing Markov random fields Computer science Bayesian methods ICM(iteration condition model Iterative algorithms Multi-temporal Remote-Sensing Images Labeling Pixel
Content Type	Text
Resource Type	Article

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