Fusion of Panchromatic and Multispectral Images via Coupled Sparse Non-Negative Matrix Factorization

In this paper, we construct a new coupled sparse non-negative matrix factorization (CSNMF) model for the fusion of panchromatic (PAN) and multispectral (MS) images. Two CSNMFs are developed for a joint sparse representation of MS and PAN images. Moreover, a sequential iterative algorithm is proposed to simultaneously find the solution to CSNMF. Because learned dictionaries can reveal the latent structure of images in spatial and spectral domains, the fused high-resolution MS images can be calculated by multiplying the dictionary of PAN image and the sparse coefficients of MS images. Some experiments are taken on simulated and real QuickBird data, and the results show that CSNMF outperforms its counterparts in both visual quality and numerical guidelines.