A novel pan sharpening method via sparse representation over learned dictionary

摘要

Remote sensing pan sharpening aims to enhance spatial resolution of multispectral image by injecting spatial details of a panchromatic image to multispectral image. In this study, a novel sparse representation based pan sharpening method is proposed to overcome the disadvantages of traditional methods such as color distortion and blurring effect. A data set acquired for each IKONOS and Quickbird satellites are used to evaluate the performance and robustness of the proposed algorithm. The proposed method is compared with four traditional methods using several quality measurement indices with reference image. The experimental results demonstrate that the proposed algorithm is competitive or superior to other conventional methods in terms of visual and quantitative analysis as it preserves spectral information and provides high quality spatial details in the final product image.