A Joint Optimization Based Pansharpening via Subpixel-Shift Decomposition

Patch-based spatial dictionary has been widely used to fuse a high-resolution panchromatic (PAN) image with a low-resolution multispectral (LMS) image under the framework of sparse representation (SR). However, patch-based dictionary in the spatial domain is not sufficient to preserve spectral information, which may lead to large spectral distortion. To solve this problem, a new spectral dictionary-based pansharpening method using subpixel-shift decomposition and joint optimization (termed PANDA) is proposed. In this method, the model of pansharpening is formulated in a decomposed spectral domain under the sparse and low-rank constraint, as a joint optimization procedure of the spectral dictionary and its coefficients. Specifically, a subpixel- shift decomposition is first constructed, to decompose the PAN image into a series of subimages with the same spatial resolution as the LMS image. Then, a new imaging model for the pansharpening problem of the LMS image and the decomposed PAN subimages is formulated, with sparse and low-rank constraints. And finally, a joint optimization procedure for the spectral dictionary and its coefficients are theoretically derived, using the spectral information provided by the LMS image and the spatial information provided by the entire PAN subimages, respectively. Experimental results on different datasets show that the pansharpening performance of the proposed PANDA method outperforms the state-of-the-art methods in both spatial and spectral domains.