Hyperspectral Image Super-Resolution Based on Spatial and Spectral Correlation Fusion

Super-resolution image reconstruction has been utilized to overcome the problem of spatial resolution limitation in hyperspectral (HS) imaging. To improve the spatial resolution of HS image, this paper proposes an HS-multispectral (MS) fusion method, which exploits spatial and spectral correlations and proper regularization. High spatial correlation between MS image and the desired high-resolution HS image is conserved via an over-completed dictionary, and the spectral degradation between them projected onto the space of sparsity is applied as the spectral constraint. The high spectral correlation between high-spatial- and low-spatial-resolution HS image is preserved through linear spectral unmixing. The idea of an interactive feedback proposed in our previous work is also used when dealing with spatial reconstruction and unmixing. Low-rank property is introduced in this paper to regularize the sparse coefficients of the HS patch matrix, which is utilized as the spatial constraint. Experiments on both simulated and real data sets demonstrate that the proposed fusion algorithm achieves lower spectral distortions and the super-resolution results are superior to those of other state-of-the-art methods.