Unsupervised Hyperspectral Band Selection by Fuzzy Clustering With Particle Swarm Optimization

Due to the lack of label information and the intrinsic complexity of hyperspectral images (HSIs), unsupervised band selection is always one of the most challenging tasks in HSI processing. Fuzzy clustering is a promising technique for unsupervised band selection, which can partition unlabeled data into groups effectively. However, due to the limits of its optimization process, standard fuzzy clustering is sensitive to initialization and easy to be trapped in a local optimum. To address the limits, a novel unsupervised band selection method is proposed, combining fuzzy clustering with particle swarm optimization (PSO). A newly designed PSO algorithm is introduced to improve the performance of fuzzy clustering band selection. Moreover, a new strategy is designed to select representative cluster centers according to the characteristics of HSIs. The experimental results indicate that the proposed method has the ability to select high-quality band subsets with good and robust performance on HSI classification.