Dynamic Cross Feature Fusion for Remote Sensing Pansharpening
Xiao Wu, Ting‐Zhu Huang, Liang-Jian Deng, Tian-Jing Zhang
Abstract
Deep Convolution Neural Networks have been adopted for pansharpening and achieved state-of-the-art performance. However, most of the existing works mainly focus on single-scale feature fusion, which leads to failure in fully considering relationships of information between high-level semantics and low-level features, despite the network is deep enough. In this paper, we propose a dynamic cross feature fusion network (DCFNet) for pansharpening. Specifically, DCFNet contains multiple parallel branches, including a high-resolution branch served as the backbone, and the low-resolution branches progressively supplemented into the backbone. Thus our DCFNet can represent the overall information well. In order to enhance the relationships of inter-branches, dynamic cross feature transfers are embedded into multiple branches to obtain high-resolution representations. Then contextualized features will be learned to improve the fusion of information. Experimental results indicate that DCFNet significantly outperforms the prior arts in both quantitative indicators and visual qualities.