摘要
针对复杂背景下的合成孔径雷达(SAR)图像的分割问题,提出一种基于非降采样Contourlet变换(NSCT)域马尔可夫(MRF)模型的算法。该算法综合利用了MRF模型在影像分割中的优势和图像的多分辨率描述的信息,采用高斯混合模型建模各个尺度的特征场,Potts模型建模各个尺度的标记场,大尺度的分割结果直接投影到小尺度上,作为分割的初始结果。实验部分与经典的阈值分割算法和马尔可夫分割算法进行比较、分析,结果表明该算法可准确地分割目标,同时保留目标的细节信息。
An algorithm of Markov Field（MRF） model based on Non-drop Sampling Contourlet Transform（NSCT） domain is proposed for the Synthetic Aperture Radar（SAR） image under complex background. Utilization of the advantages of both the MRF model and multi-resolution description of the information, Gaussian Model can be used in the characteristics field of each scale and Potts Model also used in the mark field of each scale. Then as the initial value, large-scale segmentation results can be directly projected to the small scale. In the experimental part, comparing with the classic threshold segmentation algorithm and Markov segmentation algorithm, the result shows that target can be accurately segmented while preserving more details.
出处
《计算机工程与应用》
CSCD
2013年第16期172-174,264,共4页
Computer Engineering and Applications
