It is a tough challenge to find a remote sensing image fusion method which can acquire spatial and spectral information as much as possible from panchromatic (PAN) image and multispectral (MS) image. Sparse representation (SR) can realize remote sensing image fusion better than other popular methods, which is a powerful tool for dealing with the signals of high dimensionality. In addition, to gain better fusion results without color distortion, this paper propose a remote sensing image fusion algorithm with SR and color matching in stead of the intensity hue saturation (IHS) color model and Brovey transform. The experimental results show that proposed method can make fused image with both better spatial details and spectral information compared with three well-known methods.
Sparse representation based image fusion has been widely studied recently. However, it’s not popular in some fields for the high time complexity. In this paper, a new image fusion method based on group sparse representation is proposed to overcome this problem. The K-SVD method is utilized to get the sparse representation of the source images. Therefore, it is necessary to find the best size of the group according to its property about time consuming. And there is no need to sparse all the patches once but to sparse some groups simultaneously. Because every group image vectors sparse representation is unique from the others, using the parallel-processing strategy can reduce the time badly. Besides, all dictionaries are learned from local source image vectors, so the quality of the results fused by the group sparse representation method will be better than those fused by the normal sparse representation methods. Compared with four types of state-of-the-art algorithms, the proposed method has the excellent fusion performance in experiments.