基于Chan-Vese模型的SAR图像分割  

SAR Image Segmentation Based on Chan-Vese Model

作　　者：东野长磊[1] 郑永果[1] 苏杰[1] 

机构地区：[1]山东科技大学信息科学与工程学院,青岛266510

出　　处：《数据采集与处理》2012年第2期151-155,共5页Journal of Data Acquisition and Processing

基　　金：国家高技术发展计划("八六三"计划)(2009AA0627018)重点资助项目;山东省教育厅科技计划(J08LJ10)资助项目

摘　　要：由于SAR图像存在较强的斑点噪声,使用Chan-Vese模型水平集分割方法会产生很多误分割。同时,水平集解法存在计算量大、分割速度慢的问题。在Chan-Vese模型基础上,增加新的内能项——距离正则项,得到了一种改进的曲线演化模型。避免了水平集函数的周期性更新,具有更大的迭代步长,从而加快分割速度,并且提高Chan-Vese模型的抗噪性。对该模型采用人工合成图像和真实SAR图像进行分割实验,通过比较,可看出改进模型具有较高的数值精度和较快的分割速度。对于噪声很强的图像,使用增强Lee滤波进行预处理,可以进一步提高改进模型的分割速度和效果。实验结果表明:改进Chan-Vese模型能高效快速地完成SAR图像分割,具有较高的抗噪性。Due to strong speckle noise in synthetic aperture radar （SAR） image, the Chan-Vese model level set segmentation method produces a lot of false segmentation. Meanwhile, the level set has disadvantages of large amount of computation and slow segmentation velocity. There- fore, a new internal force term-- distance regularized term is introduced to create an improved curve evolution model based on the Chan-Vese model. The model avoides the periodic updates of level set function and has a longer time step. So the segmentation speed is speeded up, and the anti-noise capability is enhanced. Then, the model is tested by processing the synthetic im- age and real SAR images. By comparison, the improved model has higher numerical accuracy and faster division speed. As for the image with strong noise, using the enhanced Lee filter can further improve the speed and effect of the segmentation model. The result shows that the improved Chan-Vese model can complete SAR image segmentation rapidly and efficiently with high robustness.

关 键 词：合成孔径雷达 图像分割Chan—Vese模型 距离正则项 增强Lee滤波 

分 类 号：TN911.73[电子电信—通信与信息系统]