Hyperspectral Super-Resolution by Coupled Spectral Unmixing

Charis Lanaras, Emmanuel Baltsavias, Konrad Schindler; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 3586-3594

Hyperspectral cameras capture images with many narrow spectral channels, which densely sample the electromagnetic spectrum. The detailed spectral resolution is useful for many image analysis problems, but it comes at the cost of much lower spatial resolution. Hyperspectral super-resolution addresses this problem, by fusing a low-resolution hyperspectral image and a conventional high-resolution image into a product of both high spatial and high spectral resolution. In this paper, we propose a method which performs hyperspectral super-resolution by jointly unmixing the two input images into the pure reflectance spectra of the observed materials and the associated mixing coefficients. The formulation leads to a coupled matrix factorisation problem, with a number of useful constraints imposed by elementary physical properties of spectral mixing. In experiments with two benchmark datasets we show that the proposed approach delivers improved hyperspectral super-resolution.