Key points are not available for this paper at this time.
In recent years, enormous efforts have been made to design image-processing algorithms to enhance the spatial resolution of hyperspectral (HS) imagery. One of the most commonly addressed problems is the fusion of HS data with higher spatial resolution multispectral (MS) data. Various techniques have been proposed to solve this data-fusion problem based on different theories, including component substitution (CS), multiresolution analysis (MRA), spectral unmixing, and Bayesian probability. This article presents a comparative review of those HS-MS fusion techniques with extensive experiments. Ten state-of-the-art HS-MS fusion methods are compared by assessing their fusion performance both quantitatively and visually. Eight data sets featuring different geographical and sensor characteristics are used in the experiments to evaluate the generalizability and versatility of the fusion algorithms. To maximize the fairness and transparency of this comparison, publicly available source codes are used, and parameters are individually tuned for maximum performance.
Yokoya et al. (Thu,) studied this question.