Quantitative metrics of full-resolution QB data.

Pansharpening aims to combine the spatial information from high-resolution panchromatic (PAN) images with the spectral information from low-resolution multispectral (LRMS) images generating high-resolution multispectral (HRMS) images. While Convolutional Neural Networks (CNNs) have shown impressive performance in pansharpening tasks, their tendency to focus more on low-frequency information will lead to suboptimal preservation of high-frequency details, which are crucial for producing HRMS images. Recent studies have highlighted the significance of frequency domain information in pansharpening, but existing methods often consider the network as a whole, overlooking the unique abilities of different layers in capturing high-frequency components. This oversight can result in the loss of fine details and limit the overall performance of pansharpening. To overcome these limitations, we propose FIAN, a novel frequency information-adaptive network designed specifically for spatial-frequency domain pansharpening. FIAN introduces an innovative frequency information-adaptive filter module that can dynamically extract frequency-domain information at various frequencies, enabling the network to better capture and preserve high-frequency details during the pansharpening process. Furthermore, we have developed a frequency feature selection strategy to accurately extract the most relevant frequency-domain information, enhancing the network’s representational power. Lastly, we present a multi-frequency information fusion module that effectively combines the frequency-domain information extracted by the filter at different frequencies with the spatial-domain information. We conducted extensive experiments on multiple benchmark datasets to evaluate the effectiveness of the proposed method. The experimental results demonstrate that our approach achieves competitive performance compared to state-of-the-art pansharpening methods.