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In this paper, we propose a multiscale Bayesian segmentation algorithm for SAR image. This approach uses a hierarchical two-level Markov random field (MRF) to represent both texture and region label over the wavelet lattice. The high level uses an isotropic multilevel logistic (MLL) random field to characterize the blob-like region formation process at each scale and the interscale dependencies over the corresponding multiresolution region. At lower level a novel causal Gaussian autoregressive (CGAR) process is proposed to describe the fill-in of multiresolution region. Once the multiscale double MRFs model is established, in term of sequential maximum a posterior (SMAP), model parameter estimate and region segmentation are performed alternately from coarse to fine scale. Our segmentation method is tested on both synthetic and ERS-1 SAR images.
Signal Processing and Its Applications, 2003. Proceedings. Seventh International Symposium on (Volume:1 )
Date of Conference: 1-4 July 2003