DCCPNet: A Dual-Branch Channel Cross-Concatenation Pan-Sharpening Network for Satellite Remote Sensing Imagery

Pan-sharpening performance has been significantly improved by deep learning, yet current networks are limited by single-branch architectures or noninteractive dual-branch designs. To surmount these limitations, a dual-branch channel cross-concatenation (DCC) pan-sharpening network, i.e., DCCPNet, is proposed, which introduces DCC blocks to facilitate bidirectional feature exchange between panchromatic and multispectral branches. Within this framework, hierarchical features are extracted through multiscale convolution residual (MCR) blocks, while complementary spatial and spectral information is adaptively fused in the DCC blocks. Comprehensive evaluations conducted on IKONOS and WorldView-2 datasets demonstrate that the proposed method achieves state-of-the-art performance across 11 quantitative metrics. Furthermore, the proposed method exhibits apparent superiority to other advanced methods in image visual effects, operational efficiency testing, and normalized difference vegetation index application, showcasing its robust competitiveness.