Fusformer: A Transformer-Based Fusion Network for Hyperspectral Image Super-Resolution

Abstract

Hyperspectral image super-resolution (HISR) is to fuse a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI), aiming to obtain a high-resolution hyperspectral image (HR-HSI). Recently, various convolution neural network (CNN) based techniques have been successfully applied to address the HISR problem. However, these methods generally only consider the relation of a local neighborhood by convolution kernels with a limited receptive field, thus ignoring the global relationship in a feature map. In this paper, we design a transformer-based architecture (called Fusformer) for the HISR problem, which is the first attempt to apply the transformer architecture to this task to the best of our knowledge. Thanks to the excellent ability of feature representations, especially by the self-attention in the transformer, our approach can globally explore the intrinsic relationship within features. Considering the specific HISR problem, since the LR-HSI holds the primary spectral information, our method estimates the spatial residual between the upsampled LR-MSI and the desired HR-HSI, reducing the burden of training the whole data in a smaller mapping space. Various experiments show that our approach outperforms current state-of-the-art HISR methods. The code is available at https://github.com/J-FHu/Fusformer.