Abstract
Pansharpening techniques fuse the complementary information from panchromatic (PAN) and multispectral (MS) images to obtain a high-resolution MS image. However, the majority of existing pansharpening techniques suffer from spectral distortion owing to the low correlation between the MS and PAN images, and difficulties in obtaining appropriate injection gains. To address these issues, this article presents a novel pansharpening method based on the variational fractional-order geometry (VFOG) model and optimized injection gains. Specifically, to improve the correlation between the PAN and MS images, the VFOG model is constructed to generate a refined PAN image with a similar spatial structure to the MS image, while maintaining the gradient information of the original PAN image. Furthermore, to obtain accurate injection gains, and considering that the vegetated and nonvegetated regions should be dissimilar, an optimized adaptive injection gain based on the normalized differential vegetation index is designed. The final pansharpened image is obtained by an injection model using the refined PAN image and optimized injection gains. Extensive experiments on various satellite datasets demonstrate that the proposed method offers superior spectral and spatial fidelity compared to existing state-of-the-art algorithms.
Highlights
Seven state-of-the-art pansharpening algorithms were used for comparison to verify the performance of the proposed algorithm: GS [13], Generalized Laplacian Pyramid with MTF-matched filter (GLP-MTF) [46], PRACS [14], the bilateral filter luminance proportional (BFLP) method [47], the full-scale regression-based injection coefficient (FSRIC) method [48], PanColorGAN [27], and Fusion-Net [28]
A novel pansharpened algorithm based on the variational fractional-order geometry (VFOG) model and optimized injection gains has been presented
The main purpose of the proposed algorithm is to minimize the spectral distortion caused by the dissimilarities between the PAN and MS images
Summary
(Yong Yang and Hangyuan Lu contributed to this work.) (Corresponding author: Shuying Huang.). To obtain the HRMS image, a feasible method is to fuse the information of the PAN and MS images using suitable algorithms. This process is known as pansharpening, which aims to improve the spatial resolution of the MS images by means of the PAN images. Pansharpening is considered as a powerful post-processing technique and has become a significant issue in the remote sensing field [9]
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