Sparse representation and PCA method for image fusion in remote sensing

Image fusion in remote sensing is an issue to fuse the texture information of panchromatic (PAN) channel and the spectral information of multispectral (MS) channels with lower spatial resolution (LR). In this paper, a method named SPCA is proposed to deal with image fusion from the perspective of sparse representation and PCA, in which the correlations both within and between channels are effectively modeled. First, the sparse representation theory is applied to remote sensing images. Second, the dictionaries of PAN and MS images are joint-learned, and a thought of PCA is applied to construct dictionaries of MS images of high spatial resolution (HR). Then the fusion images can be calculated with constructed dictionaries and sharing coefficient. Finally, the residual produced by sparse representation is interpolated as compensation. Compared with four methods in four evaluation indexes, SPCA method gives competitive or even better results on LandSat8 and QuickBird.