Unsupervised change detection with high-resolution SAR images by edge-preserving Markov random fields and graph-cuts
Moser, G ; Serpico, SB ; - ASI Sponsor
Jul - 2012
DOI: 10.1109/IGARSS.2012.6351112

journal : Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
type: Article Journal

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.

keywords : Markov random fields Unsupervised change detection expectation-maximization graph cuts line processes method of log-cumulants