A New Constrained Spectral Clustering for SAR Image Segmentation

摘要：

Pairwise constraints have been successful to be applied in traditional clustering methods.However,little progress has been made in incorporating them into spectral clustering.In this paper,we propose a new method to combine pairwise constraints with spectral clustering and apply it to sAR image segmentation.Firstly,we learn a distance metric using pairwise constraints.In doing so,an affinity matrix is obtained by the Gaussian function on the learned distance metric. Then we perform the spectral decomposition on the affinity matrix and we get the spectral features of data points.Finally,the constrained k-means is used to cluster spectral features instead of k-means used commonly.We apply the proposed method to synthetic aperture radar (SAR) image segmentation.Experimental results show that it is effective for SAR image segmentation.

关键词： Spectral clustering Constrained k-means clustering Synthetic aperture radar (SAR) Image segmentation

作者: Haishuang Zou Weida Zhou Li Zhang Caili Wu Ruochen Liu Licheng Jiao

作者单位: Institute of Intelligent Information processing & Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education,Xidian University,China,710071

会议类型: 国际会议

会议名称: 2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar(第二届亚太合成孔径雷达会议)

会议地点: 西安

会议语种:英文

页码: 680-683

在线出版日期: 2009-10-26（万方平台首次上网日期，不代表论文的发表时间）