Multipatch Progressive Pansharpening With Knowledge Distillation

In this paper, we propose a novel multi-patch and multi-stage pansharpening method with knowledge distillation, termed as PSDNet. Different from existing pansharpening methods that typically input single-size patches to the network and implement pansharpening in an overall stage, we design multi-patch inputs and a multi-stage network for more accurate and finer learning. First, multi-patch inputs allow the network to learn more accurate spatial and spectral information by reducing the number of object types. We employ small patches in the early part to learn accurate local information, as small patches contain fewer object types. Then, the later part exploits large patches to fine-tune it for the overall information. Second, the multi-stage network is designed to reduce the difficulty of the previous single-step pansharpening and progressively generate elaborate results. In addition, instead of the traditional perceptual loss, which hardly relates to the specific task or the designed network, we introduce distillation loss to reinforce the guidance of the ground truth. Extensive experiments are conducted to demonstrate the superior performance of our proposed PSDNet to existing state-of-the-art methods. Our code is available at https://github.com/Meiqi-Gong/PSDNet.