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.

最近，在合成孔径雷达（SAR）图像分割中已经极大地考虑了条件随机场（CRF）模型。该模型不仅直接考虑了以图像为条件的标记场的后验分布，而且给出了观测值之间的相互作用。在本文中，我们提出了一种基于CRF的SAR图像分割新算法。在提出的条件随机场模型中，我们在特征提取和相似性测量中共同考虑使用统计方法。利用二维小波变换的好处，我们在小波系数上定义了广义高斯分布（GGD），以提取基于纹理的特征。然后，为了改善CRF势函数，提出了一个新的一元函数，该函数与小波系数的统计特性完全匹配，并针对不同区域产生更准确的参数。作为此功能的优势，不再需要应用以前的CRF中使用的多项逻辑回归（MLR）模型。此外，利用分布函数之间的Kullback-Leibler距离（KLD），非常有效地提出了我们成对电势中的相似性度量。该方案的优越性在于，可以使用GGD的参数（与以前方法中的特征向量相比通常具有较小的大小）完全计算相似性度量。