Sparse representation over shared coefficients in multispectral pansharpening

Abstract

The pansharpening process is for obtaining an enhanced image with both high spatial and high spectral resolutions by fusing a panchromatic (PAN) image and a low spatial resolution multispectral (MS) image. Sparse Principal Component Analysis (SPCA) method has been proposed as a pansharpening method, which utilizes sparse coefficients and over-complete dictionaries to represent the remote sensing data. However, this method still has some drawbacks, such as the existence of the block effect. In this paper, based on SPCA, we propose the Sparse over Shared Coefficients (SSC), in which patches are extracted with a sliding distance of 1 pixel from a PAN image, and the MS image shares the sparse representation coefficients trained from the PAN image independently. The fused high-resolution MS image is reconstructed by K-SVD algorithm and iterations, and residual compensation is applied when the down-sampling constraint is not satisfied. The simulated experiment results demonstrate that the proposed SSC method outperforms SPCA and improves the overall effectiveness.