A new multiplicative nonnegative matrix factorization method for unmixing hyperspectral images combined with multispectral data

摘要

In these investigations, a novel algorithm is proposed for linearly unmixing hyperspectral images combined with multispectral data. This algorithm, which is used to unmix the considered hyperspectral image, is founded on nonnegative matrix factorization. It minimizes, with new multiplicative update rules, a novel cost function, which includes multispectral data and a spectral degradation model between these data and hyperspectral ones. The considered multispectral variables are also used to initialize the proposed algorithm. Tests, using synthetic data, are carried out to assess the performance of our algorithm and its robustness to spectral variability between the processed data. The obtained results are compared to those of state of the art methods. These tests prove that the proposed algorithm outperforms all other used approaches.