Sparse representation based pan-sharpening

In this paper, we propose a novel pan-sharpening method which combines classical component substitution with recently developed sparse representation. We explore the sparse representations of multispectral and panchromatic images through two dictionaries which are trained to have the same sparse representations for each high-resolution and low-resolution image patch pair. The merging procedure is implemented in sparse domain. In order to avoid spectral distortion, partial replacement is used to extract details. At the same time, the introducing of dictionary pair also reduces the distortion caused by interpolating the MS at the initialization of the fusion process. As inherent characteristics and structure of signals are reflected better via sparse representation, the proposed method can well preserve spectral and spatial details of the source images. Experimental results on IKONOS and Quickbird images demonstrate our method´s superiority in both the spatial resolution improvement and the spectral information preservation.