A New ADMM-Based Hyperspectral Unmixing Algorithm Associated with a Linear Mixing Model Addressing Spectral Variability with a Multiplicative Structure

In this paper, we propose an approach based on an Alternating Direction Method of Multipliers (ADMM) to unmix hyperspectral data using a recently proposed linear mixing model in which the spectral variability phenomenon is spectrally modeled in a multiplicative manner. This model allows for pixel-wise variation of the endmembers, resulting in different versions of the reference component spectra being considered in each pixel of the image. The proposed ADMM-based unmixing algorithm involves new iterative update rules. The investigation also evaluates the performance of the designed algorithm against some literature ones previously proposed. To this end, experiments using synthetic hyperspectral data are carried out. Overall, the obtained results prove that the proposed algorithm is very attractive for hyperspectral unmixing taking the spectral variability phenomenon into account.