Pansharpening has benefited from the development of deep learning (DL) and has achieved excellent results. However, most DL-based methods extract local features by convolutional neural networks and do not integrate global features. Moreover, these methods only extract high-frequency features on the high-pass domain (HPD) or only consider image features on the intensity domain (ID). The method that only considers features in one domain may result in insufficient extraction of spatial and spectral features. Therefore, we propose a dynamic local-global network model on dual-domains, that is, HPD ...
