Hyperspectral image resolution enhancement using high-resolution multispectral image based on spectral unmixing

In this paper, a hyperspectral (HS) image resolution enhancement algorithm based on spectral unmixing is proposed for the fusion of the high-spatial-resolution multispectral (MS) image and the low-spatial-resolution HS image (HSI). As a result, a high-spatial-resolution HSI is reconstructed based on the high spectral features of the HSI represented by endmembers and the high spatial features of the MS image represented by abundances. Since the number of endmembers extracted from the MS image cannot exceed the number of bands in least-squares-based spectral unmixing algorithm, large reconstruction errors will occur for the HSI, which degrades the fusion performance of the enhanced HSI. Therefore, in this paper, a novel fusion framework is also proposed by dividing the whole image into several subimages, based on which the performance of the proposed spectral-unmixing-based fusion algorithm can be further improved. Finally, experiments on the Hyperspectral Digital Imagery Collection Experiment and Airborne Visible/Infrared Imaging Spectrometer data demonstrate that the proposed fusion algorithms outperform other famous fusion techniques in both spatial and spectral domains.