Multiscale MRF-based Texture Segmentation of SAR Image  

作　　者：XUXin LIDeren SUNHong 

机构地区：[1]SchoolofElectronicInformation,WuhanUniversity,Wuhan430079,China [2]NationalLaboratoryforInformationEngineeringinSurveying,MappingandRemoteSensing,WuhanUniversity,Wuhan430079,China

出　　处：《Chinese Journal of Electronics》2004年第4期671-675,共5页电子学报（英文版）

摘　　要：We propose a multiscale Bayesian segmentation algorithm for SAR image in this paper. A hierarchical two-level Markov random field (MRF) is applied to represent both texture and region label over the wavelet lattice. The high level uses an isotropic Multi-level 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 posteriori (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.

关 键 词：MRF CGAR SMAP SAR 合成孔径雷达 图像处理 图像分割 

分 类 号：TN957.52[电子电信—信号与信息处理]