A Hybrid Pan-Sharpening Approach Using Nonnegative Matrix Factorization for WorldView Imageries

With the advent of WorldView series imageries (WorldView-2/3/4), it is necessary to develop new fusion approaches for remote sensing images with higher spatial and spectral resolutions. Since most existing fusion approaches are not well capable of merging multi-spectral images with eight bands, a new hybrid pan-sharpening approach is proposed in this paper. The hybrid framework integrates the multiplicative model and additive model to improve the quality of multi-spectral images. In the additive procedure, the nonnegative matrix factorization (NMF) algorithm is utilized to synthesize the intensity component for obtaining the mutual information from multi-spectral images. Then the difference information between the panchromatic image and synthetic component is injected into multi-spectral images by the spectral-adjustable weights. In the multiplicative procedure, the smoothing filter-based intensity modulation (SFIM) is used to modulate the preliminary fusion. The nonlinear fitting method is utilized to calculate the optimal parameters of the hybrid model. Visual and quantitative assessments of fused images show that the proposed approach clearly improves the fusion quality compared to the state-of-the-art algorithms.