Orthogonal Graph-regularized Non-negative Matrix Factorization for Hyperspectral Image Clustering

Abstract

As an unsupervised task, hyperspectral image (HSI) clustering separates pixels into different groups. In this paper, an orthogonal graph-regularized non-negative matrix factorization (OGNMF) algorithm is proposed for HSI clustering. Because of large size of HSI, the HSI clustering problem is computational consuming. On the other hand, non-negative matrix factorization (NMF), which is a popular multivariate analysis method, has related to light computation budget. Furthermore, the orthogonal and graph regularization, which can improve the clustering performance and capture the local structure features, are applied to NMF for better performance in HSI. Moreover, both spatial and spectral information are extracted and utilized. Our experiments show that the performance of the proposed OGNMF is better than the NMF and ONMF algorithms in HSI clustering.