Abstract
An unsupervised change-detection method that considers the spatial contextual information in a log-ratio difference image generated from multitemporal SAR images is proposed. A Markov random filed (MRF) model is particularly employed to exploit statistical spatial correlation of intensity levels among neighboring pixels. Under the assumption of the independency of pixels and mixed Gaussian distribution in the log-ratio difference image, a stochastic and iterative EM-MPM change-detection algorithm based on an MRF model is developed. The EM-MPM algorithm is based on a maximiser of posterior marginals (MPM) algorithm for image segmentation and an expectation-maximum (EM) algorithm for parameter estimation in a completely automatic way. The experiment results obtained on multitemporal ERS-2 SAR images show the effectiveness of the proposed method.
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References
Singh A(1989)Digital change detection techniques using remotely sensed data[J]. International Journal of Remote Sensing, 10(6): 989–1 003
Lu D, Mausel P, Brondízio E, et al.(2004)Change detection techniques[J]. International Journal of Remote Sensing, 25(12): 2 365–2 407
Rignot E J M, van Zyl J J(1993)Change detection techniques for ERS-1 SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 31(4): 896–906
Bazi Y, Bruzzone L, Melgani F(2005)An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images[ J]. IEEE Transactions on Geoscience and Remote Sensing, 43(4): 874–887
Dekker R J(1998)Speckle fltering in satellite SAR change detection imagery[J]. International Journal of Remote Sensing, 19(6): 1 133–1 146
Zhang Jianqing, Zhang Zuxun(1997)Digital photogrammetry[M]. Wuhan: Wuhan University Press (in Chinese)
Vaccaro R, Smits P C, Dellepiane S G(2000)Exploiting spatial correlation features for SAR image analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 38(3): 1 212–1 223
Tso B, Mather P M(2001)Classification methods for remotely sensed data[M]. London: Taylor and Francis
German D, Geman S(1984)Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6): 721–741
Marroquin J, Mitter S, Poggio T(1987)Probabilistic solution of illposed problems in computational vision[J]. Journal of the American Statistical Association, 82: 76–89
Delp E J, Comer M L(2000)The EM/MPM algorithm for segmentation of textured images: analysis and further experimental results[J]. IEEE Transactions on Image Processing, 9(10): 1 731–1 744
Redner R A, Walker H F(1984)Mixture densities, maximum likelihood and the EM algorithm[J]. SIAM Review, 26(2): 195–239
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Jiang, L., Liao, M., Zhang, L. et al. Unsupervised change detection in multitemporal SAR images using MRF models. Geo-spat. Inf. Sc. 10, 111–116 (2007). https://doi.org/10.1007/s11806-007-0051-y
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DOI: https://doi.org/10.1007/s11806-007-0051-y