When Pansharpening Meets Graph Convolution Network and Knowledge Distillation

In this article, we propose a novel graph convolutional network (GCN) for pansharpening, defined as GCPNet, which consists of three main modules: the spatial GCN module (SGCN), the spectral band GCN module (BGCN), and the atrous spatial pyramid module (ASPM). Specifically, due to the nature of GCN, the proposed SGCN and BGCN are capable of exploring the long-range relationship between the object and the global state in the spatial and spectral aspects, which benefits pansharpened results and has not been fully investigated before. In addition, the designed ASPM is equipped with multiscale atrous convolutions and learns richer local feature information, so as to cover the objects of different sizes in satellite images. To further enhance the representation of our proposed GCPNet, asynchronous knowledge distillation is introduced to provide compact features by heterogeneous task imitation in a teacher–student paradigm. In the paradigm, the teacher network acts as a variational autoencoder to extract compact features of the ground-truth MS images. The student network, devised for pansharpening, is trained with the assistance of the teacher network to transfer the important information of the expected ground-truth MS images. Extensive experimental results on different satellite datasets demonstrate that our proposed network outperforms the state-of-the-art methods both visually and quantitatively. The source code is released at
https://github.com/Keyu-Yan/GCPNet
.