Multispectral Image Fusion Using Fractional-Order Differential and Guided Filtering

Abstract

Remote sensing satellites can provide a large number of multispectral images. However, due to the limitations of optical sensors embedded in satellites, the spatial resolution of multispectral images is relatively low. Pansharpening aims to combine high-resolution panchromatic and multi-spectral images to generate high-resolution multi-spectral images. In this paper, we propose a pansharpening method based on a component substitution framework. We use fractional-order differential operators and guided filter to balance the spectral distortion and spatial information loss that occur when remote sensing image fusion. Fractional-order differentiation can better define the detailed map, and the guided filter can enhance the spectral information of the detailed map. Experiments show that the proposed method in this paper can better combine the spectral information and spatial information, as well as obtain satisfactory results in both subjective visual perception and objective object evaluation.