A Bilinear–Bilinear Nonnegative Matrix Factorization Method for Hyperspectral Unmixing

Abstract

Spectral unmixing of hyperspectral images consists of estimating pure material spectra with their corresponding proportions (or abundances). Nonlinear mixing models for spectral unmixing are of very recent interest within the signal and image processing community. This letter proposes a new nonlinear unmixing approach using the Fan bilinear-bilinear model and nonnegative matrix factorization method that takes into account physical constraints on spectra (positivity) and abundances (positivity and sum-to-one). The proposed method is tested using a projected-gradient algorithm on synthetic and real data. The performances of this method are compared to the linear approach and to the recent nonlinear approach.