A New Geometry Enforcing Variational Model for Pan-Sharpening

Abstract

In this paper, a new variational method for pan-sharpening is proposed to obtain a high-resolution multispectral (MS) image from a low-resolution MS image and a high-resolution panchromatic (PAN) image. In addition to using the data generative fidelity term and wavelet-based spectral information preserving term, we also associate the Hessian structural information of the PAN image with the desired pan-sharpened MS image to enforce geometry correspondence in the fusion process. More specifically, we introduce a new geometry enforcing term called “vectorial Hessian feature consistence” and combine it with the data generative fidelity term and wavelet-based spectral information preserving term to form an unified variational model for pan-sharpening. Then, the optimal solution of the proposed variational pan-sharpening model is effectively obtained using the fast iterative shrinkage thresholding algorithm (FISTA) method. In addition to well preserving spectral information, our algorithm is also able to eliminate some undesired blocky or blurry artifacts by incorporating the curvature information. Experimental results demonstrate that the proposed method outperforms various well-known pan-sharpening methods in terms of both excellent spatial and spectral qualities.