基于二叉树锚点的高光谱快速聚类算法

高光谱图像聚类问题一直是图像处理领域的研究热点.谱聚类算法是最流行的聚类算法之一,但其计算复杂度较大,难以处理大规模的高光谱图像数据.由于二叉树能够较快地选取锚点,因此基于二叉树锚点图,充分利用高光谱图像的光谱和空间特性,可保证聚类性能并降低计算复杂度.然而,该聚类算法一般采用有核的聚类方法,因此不可避免地引入了参数调...

Hyperspectral Fast Clustering Algorithm Based on Binary Tree Anchor Points

Hyperspectral image clustering has always been a hot topic in the field of image processing.Spectral clustering algorithm,as one of the most popular clustering algorithms,is widely used in hyperspectral image clustering.However,due to the large computational complexity of the spectral clustering algorithm,it is difficult to process large-scale hyperspectral image data.Because the binary tree can select anchor points very fast,the spectral and spatial characteristics of a hyperspectral image are fully utilized to ensure the clustering performance and reduce the computational complexity based on the binary tree anchor graph.However,the clustering algorithm generally adopts the kernel clustering method,therefore it is inevitable to introduce parameter adjustment.Thus,based on the selection of anchor points in the binary tree,we proposes a hyperspectral fast clustering algorithm based on the binary tree anchor graph.This algorithm innovatively applies the method of binary tree anchor selection and coreless clustering to the hyperspectral images.First,the binary tree is used to select some representative anchor points from the hyperspectral data.Second,a coreless similarity map is constructed based on these anchor points,which effectively avoids the artificial adjustment of the thermonuclear parameters to construct the similarity map.Third,the spectral clustering analysis is performed to obtain the clustering results.Finally,this algorithm is used for hyperspectral image clustering.This algorithm not only improves the clustering speed,but also reduces the necessity of original thermonuclear parameter adjustment.The experimental results show that the proposed algorithm can obtain better clustering accuracy in a shorter time compared with the traditional clustering algorithm.