基于超像素锚层收敛选点的高光谱图像聚类算法

Hyperspectral image clustering algorithm based on super-pixel anchor layer convergence point selection

针对传统高光谱图像聚类算法难以有效处理数据量快速增长的高光谱图像的问题,提出基于超像素锚层收敛选点的高光谱聚类算法。采用SuperPCA对原始数据进行基于超像素切割的降维;利用K-means选取具有代表性的锚点,构建基于锚点的邻接矩阵;通过无核邻近分配的方法构建相似图,避免对热核参数的调整;最后进行谱聚类分析获得聚类结果。在Indian Pines和Pavia Centre高光谱数据集进行仿真实验,结果表明该算法获得的分类图所含错分点更少,地物分布更加平滑,与当前高光谱图像聚类算法相比具有更优的聚类效果。

Aiming at the problem that traditional hyperspectral image clustering algorithms are difficult to effectively deal with hyperspectral images with rapidly increasing data volume,a hyperspectral clustering algorithm based on hyper-pixel anchor layer convergence point selection is proposed.SuperPCA is used to reduce the dimension of original data based on super pixel cutting.Selecting representative anchor points by K-means,and constructing an adjacency matrix based on anchor points.The similarity graph is constructed by the method of non-nuclear neighbor assignment to avoid the adjustment of thermonuclear parameters.Finally,spectral clustering analysis is carried out to obtain clustering results.The simulation experiments on Indian Pines and Pavia Centre hyperspectral data sets show that the classification map obtained by this algorithm contains fewer false points,and the distribution of ground objects is smoother.Compared with the current hyperspectral image clustering algorithms,this algorithm has a better clustering effect.