无需先验信息的水平集SAR图像分割方法

给出了一种基于水平集演化、无需任何先验信息的SAR图像分割方法.该方法是一种基于区域信息的统计活动轮廓模型方法,通过利用分段阶跃函数估计图像概率密度函数,克服了利用特定概率分布模型估计概率密度函数时,需要利用先验信息预先假定图像概率分布模型的问题;通过引入惩罚项,避免了费时且难于操作的水平集函数重新初始化过程.还给出了具体的数值实现方案和相关参数取值,改进了数值实现中的迭代终止条件.实验结果表明,固定使用列出的参数,无需任何人为干预,对于大多数图像都可获得令人满意的分割结果;对于少数图像,通过简单的参数调整也可得到良好结果.

SAR image segmentation using level set evolution without prior information

An SAR image segmentation method based on level set evolution without employing any prior information was proposed. The method was a statistical geometric active contour model in which region information was used. The step function was utilized to estimate the probability distribution function (PDF), so it was avoid to suppose a probability distribution model of images in advance, which required additional prior information. Further, a penalty term was introduced into the energy functional minimized by the level set evolution, then the costly re-initialization of level set function, which was also difficult to be implemented, was removed effectively. In addition, an iterated numerical scheme and the parameters setting were suggested, as well as the condition of terminating iteration was improved. Experiments demonstrate correct segmentation with proposed method and suggested parameters. For a few images whose segmentation is not well, correct segmentation can be achieved only by tuning one parameter simply.