A Novel Generalized Intensity-Hue-Saturation (GIHS) Based Pan-Sharpening Method With Variational Hessian Transferring

The popular intensity-hue-saturation (IHS) pan-sharpening method can provide high spatial quality while suffering from some spectral distortion, mainly due to the fact that it cannot estimate an accurate intensity image to substitute the original intensity image in the IHS space. To overcome this drawback, in this paper, we particularly use the modeling idea of variational complementary data fusion, and propose a novel yet highly effective pan-sharpening method with variational Hessian transferring in the generalized IHS (GIHS) transform domain, which aims to estimate a more accurate intensity image. More specifically, the novelty of proposed method consists in building a variational Hessian transferring model in the GIHS transform domain to transfer the Hessian-based spatial geometric information of panchromatic (Pan) image to the new intensity image and meanwhile take the spectral information preserving into consideration. Finally, the experimental results demonstrate the effectiveness of the proposed method which can perform higher spectral and spatial qualities, and higher efficiency than some state-of-the-art methods.