苏红军, 盛业华. 高光谱影像的改进K-均值监督式聚类分析方法[J]. 武汉大学学报 ( 信息科学版), 2012, 37(6): 640-643.
引用本文: 苏红军, 盛业华. 高光谱影像的改进K-均值监督式聚类分析方法[J]. 武汉大学学报 ( 信息科学版), 2012, 37(6): 640-643.
SU Hongjun, SHENG Yehua. Supervised K-means Clustering Analysis for Hyperspectral Imagery[J]. Geomatics and Information Science of Wuhan University, 2012, 37(6): 640-643.
Citation: SU Hongjun, SHENG Yehua. Supervised K-means Clustering Analysis for Hyperspectral Imagery[J]. Geomatics and Information Science of Wuhan University, 2012, 37(6): 640-643.

高光谱影像的改进K-均值监督式聚类分析方法

Supervised K-means Clustering Analysis for Hyperspectral Imagery

  • 摘要: 针对K-均值聚类存在的初始聚类中心不稳定、聚类数目难以确定的问题,提出利用正交投影散度(OPD)优化K-均值算法的初始聚类中心,设计了RD指标函数用于估计聚类数目k。将所提出的算法应用于高光谱影像特征提取与端元提取分析,实验结果表明,所提出算法的性能高于已有的类似算法。

     

    Abstract: We explore widely used K-means algorithm and propose two methods to improve its performance for hyperspectral clustering and analysis.A novel initialization method based on orthogonal subspace projection(OSP) is presented,which can get the suitable initial seeds for K-means clustering.In addition,we address a new cardinality estimation index which maximizes the distance ratio between intra-cluster distance and inter-cluster distance.It is used as a tool to estimate the numbers of clusters in K-means for hyperspectral data.The experimental results show that the proposed method can performs better than other traditional methods.

     

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