在遥感图像的众多分割方法中，高斯混合模型(GMM)是一种常用的图像建模方法。提出了高斯-瑞利混合模型(GRMM)可能更适合对遥感图像建模。介绍了传统高斯混合模型和高斯-瑞利混合模型的区别。比较了这两种混合模型对图像建模的结果，并用数据说明高斯-瑞利混合模型拟合图像的像素分布误差更小。采用最大熵方法确定图像的最佳分类数，采用马尔可夫随机场(MRF)方法及新的势能函数完成图像的分割，采用迭代条件模型(ICM)完成分割过程中的最大后验概率计算问题。在实验中采用了3幅遥感图像，实验过程中比较了各个图像运用高斯混合模型和高斯-瑞利混合模型的分割和拟合结果，分别通过数据和分割结果体现了该分割方法的效果。

Among the remote sensing images segmentation methods, Gaussian mixture model (GMM) is the widely used image model. Gaussian-Rayleigh mixture model (GRMM) is proposed, and it may be more suitable for remote sensing image modeling. The difference between classical GMM and GRMM is introduced. The modeling results of GMM and GRMM to the images are compared. The comparison data shows that the GRMM has less distribution errors than the GMM when modeling the images. The entropy-max method is utilized to determine the optimal class number. The Markov random field (MRF) and a new potential function is employed to segment the images. The iterative conditional model (ICM) is used to calculate the maximum posteriori probability. Three remote sensing images are utilized in the experiment, the fitting and segmentation results of GMM and GRMM are compared in the experiment process. The data and segmentation results show that the proposed method is more effective.