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基于集对分析的遥感图像K-均值聚类算法

DOI: 10.6046/gtzyyg.2012.04.14, PP. 82-87

Keywords: 集对分析,K-均值聚类算法,同异反联系度,遥感图像

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Abstract:

基于欧式距离的K-均值聚类算法是一种硬分类(把每个待辨识的对象严格地划分到某个类中)方法,面对具有不确定性和混合像元特征的遥感图像数据,传统K-均值聚类算法很难得到满意的分类结果。为解决这一难题,将集对分析(setpairanalysis,SPA)理论推广到遥感图像聚类算法,通过引入一个能统一描述同一性、差异性和对立性的同异反(identicaldiscrepancycontrary,IDC)联系度,提出了基于IDC联系度的改进的K-均值聚类算法。该方法克服了传统K-均值算法硬分类的缺陷,可以有效地提高遥感图像聚类精度。对Landsat5TM卫星数据的聚类分析实验表明,在含有混合像元的遥感图像地物覆盖分类中,改进的K-均值聚类方法的分类效果要优于传统K-均值聚类方法。

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