结合波利亚罐模型和M-H算法的遥感图像分割

Remote Sensing Image Segmentation Combining the Polya Urn Model and M-H Algorithm

图像精确分割是目前遥感图像分割的主要研究任务之一,为此,提出一种基于波利亚罐模型的遥感图像分割方法。该方法通过模拟波利亚罐模型的随机实验过程,将图像像素之间相互关系运用到分割过程中,以精确地实现遥感图像分割。首先,根据波利亚罐模型及其随机实验过程定义邻域势能函数,结合马尔科夫随机场建立刻画像素类属性的标号场模型;假设遥感图像中各类属内像素光谱测度服从各自的同一独立高斯分布,以建立其特征场模型;由Bayesian定理,构建图像统计分割模型;然后,使用Metropolis-Hastings(M-H)算法进行分割模型求解,实现遥感图像分割。分别对模拟和真实遥感图像进行分割实验,实验结果表明使用提出方法可以有效去除图像中孤立的错分像素,使分割结果更加精确;定性和定量分析结果验证了提出方法的可行性及有效性。

Accurate segmentation is one of main researches in remote sensing image segmentation. To this end, a remote sensing image segmentation method based on the Polya urn model is proposed. In this method, the Polya urn model is used to simulate the relationships between the pixel and its neighbor in segmentation processes. First, the label field model is built by combining Markov random field and a new potential function is defined by the Polya urn model. Then, the feature field is modeled on the assumption that spectral measures of pixels in each homogeneous region follow an identical and independent Gaussian distribution. Finally, the statistical image segmentation model is built based on Bayesian theorem, and the Metropolis-Hastings （M-H） algorithm is designed to simulate this model to obtain segmentation results. Simulated and real remote sensing images are both segmented by the proposed method. The segmentation results indicate that this method could remove the error pixels and improve the accuracy of segmentation. The qualitative and quantitative analyses demon- strate the feasibility and efficiency of the proposed method.