Texture-based Segmentation of High Resolution SAR Images Using Contourlet Transform and Mean Shift

摘要：

This paper presents an unsupervised texture-based segmentation algorithm which uses reduced contourlet transform sub-bands and mean shift clustering, to analysis the texture information of high resolution SAR images. One step and criteria is proposed to reduce the sub-bands and others is presented to decrease the number of dimension of the feature space. The mean shift clustering method is used to obtain the number of texture regions and the centre of the label class. Group the pixels into corresponding texture region by their simple distance to the class centre pixel. Experiments on a mixture of Brodatz texture and SAR images show the proposed algorithm of using contourlet transform and mean shift clustering gives satisfactory results.

关键词： SAR texture unsupervised image segmentation contourlet transform mean shift feature selection.

作者: Li Yingqi He Mingyi

作者单位: College of Electronic Engineering Northwestern Polytechnical University No.127 West Youyi Road, Xi An City, Shaanxi Province, P.R.China 710072

会议类型: 国际会议

会议名称: 2006 IEEE International Conference on Information Acquisition

会议地点: 山东威海

会议语种:英文

页码: 201-206

在线出版日期: 2006-08-20（万方平台首次上网日期，不代表论文的发表时间）