一种全自动的MSTAR SAR目标图像分割方法

狄利克雷过程混合模型(Dirichlet Process Mixture,DPM)作为一种非参数概率统计模型,可以有效应用于SAR图像的非监督分类。文中提出一种全自动的MSTAR坦克SAR图像分割方法。该方法首先基于DPM确定出图像中的类别数目,接着使用马尔科夫随机场(Markov Random Field,MRF)对所得图像类别概率的空间邻域关系进行描述,然后结合标号代价能量优化算法获取最终的分割结果。该方法在不需要人为指定待分割图像类别个数的同时,能较好地保证分割结果的合理性与连贯性。在MSTAR SAR数据上的实验表明了其有效性。

An Automatic Target Segmentation on SAR Image of MSTAR SAR Chips

As a nonparametric statistical model, Dirichlet process mixture (DPM) model can be effectively applied to unsupervisedclassification of SAR images. In this paper, an automatic target segmentation on SAR image of MSTAR SAR chips is proposed.First, the number of segments in the image is automatically inferred by using DPM. Then, Markov random field (MRF) is used fordescribing the spatial contextual information of different segments in the image. The final segmentation result is obtained by combining an optimized algorithm of label cost. According to our approach, the number of segments does not need to be predefined.Meanwhile, the reasonable and consistent segmentation results are achieved. The effectiveness of the proposed method is demonstrated by segmentation experiments on MSTAR SAR data sets.