Fast low-rank-matrix-based connection center evolution for large hyperspectral image clustering

Abstract

Clustering is a common data processing technique and has been increasingly applied in remote sensing processing. In recent years, a novel clustering algorithm called connection center evolution (CCE) has been proposed. To a certain extent, CCE solves the parameter sensitivity problem, which generally exists in traditional clustering algorithms. However, CCE is not feasible for handling large hyperspectral images (HSI) due to its computational bottlenecks. That is, when the size of HSI to be processed is large, the construction of the similarity matrix and the calculation of its power have very high space complexity and time complexity, respectively. To solve this problem, we propose a novel approach for large HSI datasets, named fast low-rank-matrix-based connectivity center evolution (LMCCE). The proposed LMCCE algorithm first introduces the KNN graph into CCE to construct a sparse similarity matrix, effectively reducing its space complexity. Then, symmetric non-negative matrix factorization (SymNMF) is used for the similarity matrix, which can maintain most of the information while reducing the redundancy and noise in similarity matrix. Furthermore, by performing SymNMF on the similarity matrix, the problem of calculating the power of the similarity matrix is transformed into the problem of calculating the power of a small matrix, which significantly reduces the time complexity. Experimental results on several synthetic and real HSI datasets demonstrate that the proposed LMCCE algorithm is faster than CCE, and can handle large HSI while achieving superior performance compared to state-of-the-art clustering methods.