NDLI: Unsupervised change detection with high-resolution SAR images by edge-preserving Markov random fields and graph-cuts

Content Provider	IEEE Xplore Digital Library
Author	Moser, G. Serpico, S.B.
Copyright Year	2012
Description	Author affiliation: University of Genoa, Dept. of Naval, Electrical, Electronic, and Telecommunications Eng. (DITEN), Via Opera Pia 11a, I-16145 (Italy) (Moser, G.; Serpico, S.B.)
Abstract	Change detection techniques represent important tools for environmental monitoring and damage assessment after environmental disasters. However, change detection methods that were found accurate for coarser-resolution SAR are often ineffective with current very high resolution (VHR) satellite SAR due to the need to suitably model the contextual and geometrical information associated with VHR data. In this paper, a novel unsupervised change detection technique is proposed for VHR SAR based on Markov random fields (MRFs), line processes, and a dictionary of SAR-specific probability density models. The estimation of the parameters of the proposed MRF model is carried out through the expectation-maximization algorithm and the method of log-cumulants. Graph cuts are used to minimize the energy function of the MRF model because of their capability to approach global minima or strong local minima in acceptable computation times. The proposed method is experimented with COSMO-SkyMed images acquired before and after an earthquake.
Starting Page	1984
Ending Page	1987
File Size	921450
Page Count	4
File Format	PDF
ISBN	9781467311601
ISSN	21536996
e-ISBN	9781467311595
e-ISBN	9781467311588
DOI	10.1109/IGARSS.2012.6351112
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-22
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Synthetic aperture radar Image edge detection Dictionaries Estimation Computational modeling Accuracy Visualization method of log-cumulants Unsupervised change detection Markov random fields line processes graph cuts expectation-maximization
Content Type	Text
Resource Type	Article

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