Anisotropic variational pansharpening model based on fractional-order derivatives

Panchromatic sharpening is a data fusion technique that combines high-resolution sresolution multispectral (HRMS) imagery. This paper proposes a novel panchromatic sharpening method that aims to mitigate texture loss in the fused image by incorporating fractional derivatives. The proposed model integrates three key components: a fractional derivative-guided regularization term, a PAN constraint term, and a traditional spectral fidelity term. The existence and uniqueness of the model’s minimum is rigorously proven within the corresponding functional space. An iterative method based on the discrete Fourier transform is employed to solve the model in the frequency domain. Experimental results, based on five real-world datasets, validate the effectiveness of the proposed method. A variety of spatial and spectral metrics are used to quantitatively assess the quality of the panchromatic sharpening results. The findings indicate that the proposed method significantly enhances sharpening performance and improves both spatial and spectral image quality compared to other widely used panchromatic sharpening techniques.