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。“,”We explore widely used K-means algorithm and propose two methods to improve its per- formance for hyperspectral clustering and analysis. A novel initialization method based on orthogo- nal 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.