Hypersharpening by a Multiplicative Joint-Criterion NMF Method Addressing Spectral Variability

Abstract

In this work, a hypersharpening approach, creating fused hyperspectral remote sensing images with high spatial and spectral resolutions, is introduced. This approach, linked to linear spectral unmixing (LSU) methods and based on a multiplicative nonnegative matrix factorization (NMF) technique, extends the Joint-Criterion NMF (JCNMF) algorithm, by addressing the spectral variability phenomenon. This method is designed for combining low spatial resolution hyperspectral and high spatial resolution multispectral data. It optimizes the considered criterion that deals with the spectral variability phenomenon by using a specific structure of involved matrices. The introduced algorithm, which uses multiplicative and iterative update rules, is applied to realistic synthetic data, and its effectiveness, in the spatial and spectral domains, is evaluated by considering commonly used assessment protocol and performance criteria. The obtained results prove that the introduced algorithm yields fused hyperspectral data with good spectral and spatial fidelities. These results also illustrate that the proposed algorithm significantly outperforms two tested literature ones that do not take the spectral variability phenomenon into account.