Unsupervised nonlinear hyperspectral unmixing based on the generalized bilinear model

Abstract

Most nonlinear unmixing algorithms are based on the nonlinear mixing models with different forms. This paper focuses on the well-known generalized bilinear model (GBM). Though the GBM has shown interesting and promising for nonlinear unmixing, currently almost all the GBM-based unmixing algorithms are supervised. That is, the endmembers must be assumed known in advance. This paper develops an unsupervised nonlinear unmixing method based on the GBM, which can obtain the endmember, abundances and nonlinearity coefficients simultaneously. In the proposed method, the projected-gradient (PG) algorithm are utilized to alternately solve two nonnegative matrix factorization problems. The former updates the endmembers while the latter updates the abundances as well as the nonlinearity coefficients. Experimental results show that the proposed algorithm provide good performance in term of both endmember estimation and abundances estimation comparing with other state-of-the-art algorithms.