A Unified Pansharpening Model Based on Band-Adaptive Gradient and Detail Correction

Pansharpening is used to fuse a panchromatic (PAN) image with a multispectral (MS) image to obtain a high-spatial-resolution multispectral (HRMS) image. Traditional pansharpening methods face difficulties in obtaining accurate details and have low computational efficiency. In this study, a unified pansharpening model based on the band-adaptive gradient and detail correction is proposed. First, a spectral fidelity constraint is designed by keeping each band of the HRMS image consistent with that of the MS image. Then, a band-adaptive gradient correction model is constructed by exploring the gradient relationship between a PAN image and each band of the MS image, so as to adaptively obtain an accurate spatial structure for the estimated HRMS image. To refine the spatial details, a detail correction constraint is defined based on the parameter transfer by designing a reduced-scale parameter acquisition model. Finally, a unified model is constructed based on the gradient and detail corrections, which is then solved by an alternating direction multiplier method. Both reduced-scale and full-scale experiments are conducted on several datasets. Compared with state-of-the-art pansharpening methods, the proposed method can achieve the best results in terms of fusion quality and has high efficiency. Specifically, our method improves the SAM and ERGAS metrics by 17.6% and 21.2% respectively compared to the traditional approach with the best average values, and improves these two metrics by 4.3% and 10.3% respectively compared to the learning-based approach with the best average values.

Automated summary

A unified pansharpening model based on band-adaptive gradient and detail correction is proposed in this study, achieving accurate spatial structure for the estimated HRMS image. By exploring gradient relationship and defining detail correction constraint, the proposed method outperforms state-of-the-art pansharpening methods in terms of fusion quality and computational efficiency.