Title :
Unsupervised Change Detection on SAR Images Using Triplet Markov Field Model
Author :
Fan Wang ; Yan Wu ; Qiang Zhang ; Peng Zhang ; Ming Li ; Yunlong Lu
Author_Institution :
Remote Sensing Image Process. & Fusion Group, Xidian Univ., Xian, China
Abstract :
The triplet Markov field (TMF) model is powerful in the nonstationary synthetic aperture radar (SAR) image analysis. Taking the speckle noise and the correlation of nonstationarities in two multitemporal SAR images into account, we propose a change-detection method based on the TMF model in this letter. The third field U in the TMF model is redefined to describe the nonstationary textural similarity between the two images for change detection. The corresponding prior energy of (X, U) is reconstructed. The adaptive weight parameter in prior energy is introduced to cope with the detection tradeoff issue. An automatic estimation of the parameter is obtained with low level of complexity. The Bayesian maximum posterior marginal criterion is utilized with the TMF model to obtain change detection. Experimental results on real SAR images validate the superiority of the proposed TMF method over the Markov random field method.
Keywords :
geophysical image processing; geophysical techniques; image denoising; radar imaging; synthetic aperture radar; Bayesian maximum posterior marginal criterion; Markov random field method; SAR images; TMF model; change-detection method; multitemporal SAR images; nonstationary SAR image analysis; parameter automatic estimation; speckle noise; synthetic aperture radar; tradeoff issue detection; triplet Markov field model; unsupervised change detection; Analytical models; Estimation; Hidden Markov models; Image segmentation; Markov processes; Remote sensing; Synthetic aperture radar; Nonstationary anisotropic model; SAR image change detection; synthetic aperture radar (SAR); triplet Markov field model;
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
DOI :
10.1109/LGRS.2012.2219494