Dual-Domain Dynamic Local–Global Network for Pansharpening

Abstract

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 network and do not integrate global features. Moreover, these methods only extract high frequency features on the high-pass domain or only consider image features on the intensity domain. 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 domain, i.e., high-pass domain and intensity domain. The dynamic local-global feature extraction block (DLGB) is designed to dynamically integrate local and global features to improve the representation capability of the network. To decrease the computational complexity of global feature extraction, a lightweight biaxial nonlocal attention (BNLA) that captures global spatial features in horizontal and vertical directions is proposed. Experiments on GeoEye-1, QuickBird and WorldView-3 datasets show that the proposed method presents better fusion performance on objective evaluation indexes and subjective perception.