国土资源遥感Issue(4)：82-87,6.DOI:10.6046/gtzyyg.2012.04.14
基于集对分析的遥感图像K-均值聚类算法
SPA-based K-means Clustering Algorithm for Remote Sensing Image
摘要
Abstract
K - means clustering algorithm is a kind of hard classification based on the Euclidean distance, with each data point assigned to a single cluster. Due to the uncertainty and mixed pixels in remote sensing image,it is difficult for the traditional K - means clustering algorithm to obtain satisfactory classification results. To overcome this drawback, the authors applied the SPA ( set pair analysis) theory to the clustering algorithm of remote sensing image. The IDC ( identical discrepancy contrary ) connection degree model, which can descript unitarily the identity, discrepancy and opposition,was employed to improve K -means clustering algorithm. The improved algorithm has overcome the limitation of K - means clustering algorithm to certain extent. Clustering analysis experiments of Landsat TM image show that the improved K - means clustering algorithm is superior to K - means in classification accuracy of ground cover class components of mixed pixels.关键词
集对分析/K-均值聚类算法/同异反联系度/遥感图像Key words
set pair analysis(SPA) / K - means clustering algorithm/ identical discrepancy contrary(IDC) connection degree/ remote sensing image分类
信息技术与安全科学引用本文复制引用
谢相建,赵俊三,陈学辉,袁思..基于集对分析的遥感图像K-均值聚类算法[J].国土资源遥感,2012,(4):82-87,6.基金项目
国家自然科学基金"面向对象的土地利用空间多尺度耦合机理研究"(编号:41161062)资助. （编号:41161062）
