Super-Resolution for Hyperspectral and Multispectral Image Fusion Accounting for Seasonal Spectral Variability
This addresses the issue of seasonal spectral variability in remote sensing image fusion, which is an incremental improvement over existing methods that neglect such variability.
The paper tackles the problem of spectral variability in hyperspectral-multispectral image fusion by introducing a novel strategy that combines unmixing with a parametric model for variability, resulting in significant performance improvements under spectral variability and state-of-the-art performance otherwise.
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.