Hyperspectral data multi-sharpening based on linear-quadratic nonnegative matrix factorization

In this paper, we propose a new multi-sharpening approach for improving the spatial resolution of hyperspectral data. This approach, based on the linear-quadratic spectral unmixing concept, uses a linear-quadratic nonnegative matrix factorization multiplicative algorithm. Our method first consists in unmixing the low spatial resolution hyperspectral data and high spatial resolution multispectral data. The obtained high resolution spectral and spatial parts of information are then recombined, according to the linear-quadratic mixing model, in order to obtain unobservable multi-sharpened high spatial resolution hyperspectral data. Experiments, based on realistic synthetic and real data, are carried out to evaluate the performance of the proposed approach and of linear nonnegative matrix factorization-based approaches from the literature. We show that our proposed approach significantly outperforms the used literature methods.