Spatial-Frequency Fusion Network With Learnable Fractional Fourier Transform for Remote Sensing Imaging Enhancement

Atmospheric haze significantly degrades the quality of remote sensing images, reducing visibility, distorting spectral information, and impairing downstream tasks such as land cover classification and infrastructure layout analysis. To overcome these challenges, this article proposes a novel spatial–frequency fusion network (termed SFFNet) with a learnable fractional Fourier transform for efficient remote sensing imaging enhancement. In the spatial domain, the SFFNet uses a multiscale spatial pyramid pooling block to capture both fine-grained details and global contextual information, while residual connections ensure robust feature learning and spatial detail preservation. In the frequency domain, a self-learned fractional Fourier transform module adaptively extracts haze-relevant features, leveraging a learnable parameter to dynamically adjust the fractional order of the transform. Furthermore, an attentive …

Recommended citation: W Xu, M Liang, Y Lu, R Gao, D Yang. (2025). Spatial-Frequency Fusion Network With Learnable Fractional Fourier Transform for Remote Sensing Imaging Enhancement. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://ieeexplore.ieee.org/abstract/document/11071378/