FMPM-DNet: Hyperspectral Pansharpening Dynamic Network Based on Feature Modulation and Probability Mask

Currently, most Hyperspectral (HS) pansharpening methods have two problems, namely the lack of consideration the spatial variations of HS images and inaccurate feature reconstruction in multi-channel complex mapping relationships, leading to spectral and spatial distortions in the fusion results. To address these issues, we propose a dynamic network based on feature modulation and probability mask (FMPM-DNet) for HS pansharpening, including two stages of spectral-spatial feature modulation and feature reconstruction. In the first stage, to increase the feature representation ability of the model, a wave function is defined based on complex transformation to convert spatial features into wave-like features. On this basis, considering the spatial variations of HS images, a dynamic feature modulation unit (DFMU) is constructed to achieve adaptive modulation and coarse fusion of features by dynamically generating spectral-spatial correction matrix. In the second stage, a feature probability mask unit (FPMU) is designed to realize global feature embedding at different depths and local feature embedding at the same depth to obtain refined fused features. Extensive experiments on three widely used datasets demonstrate that the proposed FMPM-Net achieves significant improvements in both spatial and spectral quality metrics compared to some state-of-the-art (SOTA) methods.