Multivariate Regression-Based Pan-Sharpening With Low Rank Regularization

Abstract

Pan-sharpening is a fusion task of exploiting the spectral information in low resolution multispectral images (LRMS) with spatial information in a corresponding high resolution panchromatic image (PAN). Under the component substitution framework, a multivariate regression based fidelity term is presented to enforce high resolution spatial detail injection, while a low rank regularization term is proposed to capture intrinsic structures of latent high-resolution multispectral images (HRMS). To this end, a joint optimizing pan-sharpening model is proposed to establish a trade-off mechanism between the spatial detail injection and spectral-spatial preserving capacity. Finally, the Augmented Lagrangian Multiplier (ALM) method is used to develop a pan-sharpening algorithm. Experiments demonstrate that the proposed algorithm can achieve higher spatial and spectral resolution than several state-of-the-art methods.