Low-rank and sparse matrix decomposition-based pan sharpening

Abstract

This paper proposes a remote sensing image pan-sharpening method from the perspective of low-rank and sparse matrix decomposition. Based on the characteristic of multispectral (MS) images, the low spatial resolution information of MS images is modeled as low-rank, and the high spectral resolution information of MS images is modeled as sparse. First, the low-rank and sparse matrix decomposition algorithm is applied to the resampled MS images to extract the sparse component i.e. the high spectral resolution information. Second, the standard PCA fusion method is applied on the low-rank component to obtain the rough pan-sharpened MS images. Finally, adding the sparse MS images component on the rough result and one can get the final fused product. Experimental results demonstrate that the proposed method is competitive or even better than some other methods.