  Image fusion combines data from different heterogeneous sources to obtain
more precise information about an underlying scene. Hyperspectral-multispectral
(HS-MS) image fusion is currently attracting great interest in remote sensing
since it allows the generation of high spatial resolution HS images,
circumventing the main limitation of this imaging modality. Existing HS-MS
fusion algorithms, however, neglect the spectral variability often existing
between images acquired at different time instants. This time difference causes
variations in spectral signatures of the underlying constituent materials due
to different acquisition and seasonal conditions. This paper introduces a novel
HS-MS image fusion strategy that combines an unmixing-based formulation with an
explicit parametric model for typical spectral variability between the two
images. Simulations with synthetic and real data show that the proposed
strategy leads to a significant performance improvement under spectral
variability and state-of-the-art performance otherwise.
