一种基于非参数密度估计和马尔可夫上下文的SAR图像分割算法

在研究传统的基于参数的合成孔径雷达(SAR)图像统计模型基础上,为了精确估计高分辨率SAR图像的统计分布,该文提出了一种结合基于核函数的非参数估计和马尔可夫上下文的SAR图像分割算法。该算法首先采用基于核函数的非参数方法估计SAR图像的统计分布,然后将此统计量作为图像分割的似然函数,利用马尔可夫上下文约束进行SAR图像分割。该文通过软件仿真对新算法和基于参数的统计模型的算法的效果进行了比较。研究发现,基于核函数的非参数估计方法仅仅依赖实际数据,在无法准确获取分布函数解析式的情况下往往具有更好的效果。实验证明,基于核函数的非参数估计方法对高分辨率SAR图像中较为复杂的场景如城区的提取取得了更为满意的结果。

A Segmentation Method of SAR Images Based on Non-parametric Density Estimate and Markovian Contexture

Aiming at giving a precise estimation of the statistic distribution of high-resolution Synthetic Aperture Radar (SAR) images, a segmentation method of SAR images using technique of non-parametric density estimate with kernel method and Markovian contexture is proposed in this paper, after studying the traditional models based on parametric technique. First, a non-parametric density estimate method based on kernel function is adopted to estimate the statistic distribution of the SAR images, and then, the SAR images is segmented with Markovian contexture by maximizing a MAP estimator, taking the former estimation as its likelihood term. And the results of the new proposed method and methods based on parametric statistical models are compared by software simulation. It shows that non-parametric density estimate technique based on kernel function can provide better results by just depending on real data, when there is no available analytical distribution function. Experiments on real SAR images also show that the non-parametric method can model the complex scenes of high-resolution SAR images such as urban areas well and get better results of segmentation.