Super-Resolution for Hyperspectral and Multispectral Image Fusion Accounting for Seasonal Spectral Variability

TL;DR

This paper presents a novel hyperspectral-multispectral image fusion method that explicitly models seasonal spectral variability, significantly improving fusion quality under varying spectral conditions.

Contribution

It introduces an unmixing-based fusion approach with a parametric model for spectral variability, addressing a key limitation of existing methods.

Findings

Significant performance improvement under spectral variability

State-of-the-art results in synthetic and real data simulations

Effective handling of seasonal spectral changes

Abstract

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.