Abstract
Convolutional neural networks (CNNs) have achieved impressive performance for hyperspectral (HS) and multispectral (MS) image fusion in recent years. They extract features by local filters, which is limited to explore long-range dependency in input images. However, long-range dependence is an import cue for HS and MS image fusion, as it contributes to exploration of spatial self-similarity and spectral dependence. To take advantage of long-range dependence, we propose a spectral-spatial transformer (SST) for MS and HS image fusion. The experimental results demonstrate the high performance of the proposed approach compared to some state-of-the-art methods.
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