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Pansharpening aims to fuse a registered high-resolution panchromatic image(PAN) with a low-resolution hyperspectral image (LR-HSI) to generate anenhanced HSI with high spectral and spatial resolution. Existing pansharpeningapproaches neglect using an attention mechanism to transfer HR texture featuresfrom PAN to LR-HSI features, resulting in spatial and spectral distortions. Inthis paper, we present a novel attention mechanism for pansharpening calledHyperTransformer, in which features of LR-HSI and PAN are formulated as queriesand keys in a transformer, respectively. HyperTransformer consists of threemain modules, namely two separate feature extractors for PAN and HSI, amulti-head feature soft attention module, and a spatial-spectral feature fusionmodule. Such a network improves both spatial and spectral quality measures ofthe pansharpened HSI by learning cross-feature space dependencies andlong-range details of PAN and LR-HSI. Furthermore, HyperTransformer can beutilized across multiple spatial scales at the backbone for obtaining improvedperformance. Extensive experiments conducted on three widely used datasetsdemonstrate that HyperTransformer achieves significant improvement over thestate-of-the-art methods on both spatial and spectral quality measures.Implementation code and pre-trained weights can be accessed athttps://github.com/wgcban/HyperTransformer.

HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening