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Recently, the conditional random field (CRF) model has been greatly considered in synthetic aperture radar (SAR) image segmentation. This model not only directly considers the posterior distribution of the label field conditioned on images but also gives the interactions between the observations. In this paper, we propose a new CRF-based algorithm for SAR image segmentation. We consider the statistical approach jointly in feature extraction and similarity measurement in the proposed conditional random field model. Using the benefit of the 2-D wavelet transform, we define the generalized Gaussian distribution (GGD) on the wavelet coefficients to extract texture-based features. Then, to improve the CRF potential functions a new unary function is proposed which exactly matches the statistical properties of the wavelet coefficients and produces more accurate parameters for different regions. As the advantage of this function, it is no longer necessary to apply the multinomial logistic regression (MLR) model used in previous CRFs. Moreover, using the Kullback–Leibler distance (KLD) between distribution functions, the similarity measure in our pairwise potential is proposed very effectively and efficiently. The superiority of this scheme is that the similarity measure can be entirely computed using the parameters of the GGD that are typically of small size compared with the feature vectors in the previous methods. Comprehensive experiments on both synthetic and real SAR images indicate that our proposed algorithm achieves accuracy improvement in SAR image segmentation.