Hyperspectral and Multispectral Image Fusion Based on Spectral Low Rank and Non-Local Spatial Similarities

Fusing a hyperspectral image (HSI) with a multispectral image (MSI) of the same scene has become a popular way to increase the spatial resolution of HSI. In this paper, we propose a novel HSI and MSI fusion method (termed as the SSS), which is based on spectral low rank and non-local spatial similarities. Firstly, to exploit the high spectral correlations of the desired high spatial resolution HSI, we formulate the fusion problem as the estimation of low-dimensional spectral subspace and coefficients. Since the HSI preserves most of spectral information, the spectral subspace is estimated from HSI via singular value decomposition. With the spectral subspace known, we plug a state-of-the-art denoising algorithm, weighted nuclear norm minimization, into the alternating direction method of multipliers to estimate the coefficients, which can effectively promote the non-local similarities of desired high spatial resolution HSI. Experiments demonstrate that our method is competitive to the state-of-the-art approaches.