Abstract: The objective of pansharpening is to fuse low-resolution multispectral images (LRMS) and their corresponding high-resolution panchromatic images (PAN) acquired by the same remote sensing satellite to generate high-resolution multispectral images (HRMS).Existing networks overly rely on the feature extraction and fusion capabilities of deep learning,failing to focus on the advantageous features of each modality and neglecting the distinct representations inherent in multimodal data,which leads to excessive redundant features in the final output.To extract features that independently express desired representations,reduce redundant information,and better integrate the complementary information from both modalities,this paper proposes a novel pyramid feature decoupling extraction and fusion network for pansharpening,effectively enhancing the clear representation of spectral and texture details in images.First,inspired by the divide-and-conquer concept,the network decouples and separately extracts spectral and texture information,employing different attention mechanisms to capture the unique details of each modality.Then,a cross-modal feature fusion module strengthens the interaction between features of different modalities,enabling the network to acquire complementary information while eliminating redundancy.Finally,based on a pyramid structure,the network performs feature extraction and fusion operations at multiple spatial scales,achieving outstanding results.Extensive experiments conducted on the GaoFen-2 and WorldView-3 satellite datasets demonstrate that the proposed network significantly outperforms state-of-the-art approaches,providing sub-stantial improvements for the pansharpening task.

Key words: remote sensing, pansharpening, pyramid, feature decoupling, attention mechanism