Fuzzy Embedded Clustering Based on Bipartite Graph for Large-Scale Hyperspectral Image

Abstract

Hyperspectral image (HSI) clustering has been widely used in the field of remote sensing. However, most traditional clustering algorithms are not suitable for dealing with large-scale HSI due to their low clustering performance and high computational complexity. In this letter, we propose a novel fuzzy clustering algorithm, called fuzzy embedded clustering based on the bipartite graph (FECBG), to efficiently deal with large-scale HSI clustering problems. First, we propose the FECBG method that incorporates fuzzy clustering with the nonnegative regularization term based on the bipartite graph into a unified model, which has good clustering performance and reduces the sensitivity of fuzzy clustering to the initial cluster centers. Second, we adopt the fast spectral embedded method to obtain the low-dimensional representation of HSI data, to reduce the computational complexity. At last, we add the nonnegative regularization term based on the bipartite graph to fuzzy clustering, to constrain the solution space of the fuzzy membership matrix. Experimental results on several HSI data sets have demonstrated the efficiency and effectiveness of the proposed FECBG algorithm.