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To obtain high-resolution hyperspectral data, spectral super-resolution is a popular computational imaging technique directly from high-resolution multispectral images. Besides sparse recovery, deep learning-based methods perform well in the past years for their powerful nonlinear mapping from multispectral to hyperspectral domains. However, convolutions in deep learning only focus on local information and have been blamed for the neglect of long-range relationships. Nowadays, transformer has been attracting great interest for its global attention to long-range interaction. In this study, we propose a dense spectral transformer with ResNet to achieve spectral super-resolution for multispectral remote sensing images. Combining transformer with ResNet meets the need for 3D data handling to remote sensing images as well as learning long-range relationships. Dense connection helps model exploit features from …