Dirichlet过程混合模型SAR图像分割算法

传统的合成孔径雷达(SAR)图像参数化有限模型都有其特定的物理背景或者数学假设,很难准确估计SAR图像中各地物的密度分布,为了解决这一问题,提出一种基于非参数化无限混合模型的SAR图像分割方法,该方法利用Dirichlet过程对SAR图像进行建模,进一步采用非参数化Bayes模型分割包含复杂地物目标的SAR图像。Dirichlet分布作为一种基于分布的分布可以确定不同类别的先验概率,由样本估计出密度函数来描述图像,从而可以更精确地分割各类地物。该算法在模拟图像与真实SAR图像上进行了比对测试,实验结果验证了Dirichlet过程混合模型SAR图像分割算法的有效性和稳健性。

Dirichlet process mixture model SAR image segmentation algorithm

It is difficult to segment SAR image because of the speckle noise.The traditional segmentation method of finite mixture model has been adopted extensively in SAR image segmentation.However,both the performance and the robustness are not good enough.A non-parametric Bayesian infinite mixture model,called Dirichlet Process Mixture Model(DPMM) clustering method is proposed to segment SAR image.The proposed model can simulate the intrinsic property of SAR image and the segmentation method can determine the number of clusters automatically.Experiments on both simulated data and real data are carried out.The results verify the effectiveness and robustness of the proposed method in comparison with the traditional methods.