Fusion of remote sensing images based on dictionary learning

Remote sensing image pan-sharpening is an appropriate way to fuse panchromatic (PAN) image with high spatial/low spectral resolution and multispectral (MS) image with low spatial/high spectral resolution to obtain a high spatial/high spectral resolution MS image (HRMS). Spectral information preservation and spatial resolution enhancement are the key issues of pan-sharpening problem. We propose a novel pan-sharpening method to decrease the spectral distortion of traditional methods inspired by sparse representation theory. Unlike previous works, details which must be injected to MS image should be extracted directly from a so-called `detail dictionary', trained by high frequency information of PAN image. With two proposed assumptions, we prove that the sparse coefficients of details on the detail dictionary can be obtained from a low pass version of PAN image and the MS image. We use QuickBird and WorldView-2 data sets to evaluate the proposed method. The simulation results show that the proposed method can effectively preserve spectral information and also improve the spatial resolution. The results show that the performance of proposed method is better than the state-of-the-art and TSSC methods quantitatively and perceptually.