Hyperspectral and panchromatic image fusion using unmixing-based constrained nonnegative matrix factorization

Image fusion is an important technique in remote sensing, as it could effectively combine the high spatial and the high spectral resolutions in order to obtain the complete and accurate description of the observed scene. To date, many image fusion techniques have been developed. However, the available methods could hardly produce the satisfactory results in dealing with the fusion between the hyperspectral image and panchromatic image, especially in the spectral aspect. Therefore, in this paper, a new fusion approach, called unmixing-based constrained nonnegative matrix factorization (UCNMF), is proposed. This approach uses the NMF unmixing technique to generate the abundance matrix and uses the panchromatic image to sharpen the the material maps. The constrained term aiming at preserving the spectral information is added and the fusion problem is turned into a constrained optimization problem. Additionally, a projected gradient algorithm aiming at get the numerical solution of the optimization problem is presented. Finally, three groups of experiments are given to demonstrate that the proposed fusion method could be recognized as an effective technique in hyperspectral image fusion.