A texture feature fusion-based segmentation method of SAR images

摘要：

Presents a new method for segmentation of synthetic aperture radar (SAR) images. A Gaussian autoregressive (GAR) model under a multiresolution painvise Markov framework can be proposed based on texture feature fusion images from in part gray level co-occurrence probability statistics, we examine the texture segmentation of SAR image suing the multiresolution maximization of the posterior marginal (MPM) estimate with corresponding unsupervised segmentation algorithm on those texture feature fusion images. This method not only use of pixel gray level information, but also the use of pixel space location information, reducing the speckle noise effect for the segmentation. For some SAR images, compared with multiresolution pariwise Markov-GAR model texture segmentation based on gray level images, the results of experimentation show that the segmentation precision can be improved by the method in this paper.

关键词： Gray level co-occurrence Matrices Texture feature fusion Painvise Markov random field model Multiresolution MPM Texture segmentation

作者: Baoli Liu

作者单位: Xijing University Xian,China

会议类型: 国际会议

会议名称: 2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics(第二届智能人机系统与控制论国际学术会议 IHMSC 2010)

会议地点: 南京

会议语种:英文

页码: 317-320

在线出版日期: 2010-08-26（万方平台首次上网日期，不代表论文的发表时间）