Super-resolution image reconstruction has been utilized to overcome the problem of spatial resolution limitation in hyperspectral imaging. To improve spatial resolution of hyperspectral image, this paper proposes a hyperspectral-multispectral fusion method, which exploits spatial and spectral correlation and proper regularization. High spatial correlation between multispectral image and the desired high resolution hyperspectral 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 and low spatial resolution hyperspectral image is preserved through linear spectral unmixing. The idea of interactive feedback proposed in our previous work [1] is also used when dealing with spatial reconstruction and unmixing. Low rank property is introduced in this paper to regularize the sparse coefficients of hyperspectral patch matrix, which is utilized as the spatial constraint. Experiments on both simulated and real datasets demonstrate that the proposed fusion algorithm achieves lower spectral distortions and the super-resolution results are superior to other state-of-art methods. (image reconstruction, hyperspectral, remote sensing, sparse representation, unmixing).

Runtime: 2018 - 2019