Hyperspectral image unsupervised classification using improved connection center evolution

Abstract

Unsupervised classification plays an important role in hyperspectral image(HSI) applications. However, most clustering methods used for HSI classification face the problem of tuning parameters carefully. Recently, a novel clustering algorithm named connection center evolution (CCE) has been proposed and achieved great success regarding this problem. However, for large-scale HSI data with too many pixels, CCE suffers from large memory costs for constructing the similarity matrix. In this paper, we proposed an improved version for CCE, which is inspired by the fact that the underlying data distribution can be expressed as a few weekly correlated clusters. The proposed version treats the original data as a few representative sets, then uses the clustering center of each set to construct the similarity matrix. This new version breaks, for the first time, the limitation of CCE with regards to handling large-size HSI datasets. Extensive experimental results on synthetic data and real HSI datasets show that the proposed algorithm is insensitivity to the setting of parameters, able to suggest the optimal number of clusters, and can achieve superior performance compared to state-of-the-art clustering methods.