Pansharpening through orthogonal projection of data

With the increase in the amount of satellite data particularly in the form of satellite images, the need to fuse heterogeneous imagery has become an important research area. Pansharpening is an image fusion method that involves fusing a high spatial resolution panchromatic imagery and a high spectral resolution multispectral imagery to obtain an image that possesses spatial and spectral data both in high resolution. In this paper, a pansharpening method based on a classical information-theoretic result of orthogonal projection between two sets of correlated data is proposed. The originality of the study lies in the application of the information-theoretic approach to pansharpening which has not been reported to date. The proposed method which is illustrated using IKONOS data is also compared favorably with existing pansharpening methods such as IHS, Brovey, PCA, SFIM, HPF, and Multi methods using standard evaluation criteria, such as Chi-square test (X2), R2 test, RMSE, SNR, spectral discrepancy (SD) and ERGAS.