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Source-Aware Spatial-Spectral-Integrated Double U-Net for Image Fusion
In image fusion tasks, pictures from different sources possess distinctive
properties, therefore treating them equally will lead to inadequate feature
extracting. Besides, multi-scaled networks capture information more
sufficiently than single-scaled models in pixel-wised problems. In light of
these factors, we propose a source-aware spatial-spectral-integrated double
U-shaped network called Net. The network is mainly composed of a
spatial U-net and a spectral U-net, which learn spatial details and spectral
characteristics discriminately and hierarchically. In contrast with most
previous works that simply apply concatenation to integrate spatial and
spectral information, a novel structure named the spatial-spectral block
(called Block) is specially designed to merge feature maps from
different sources effectively. Experiment results show that our method
outperforms the representative state-of-the-art (SOTA) approaches in both
quantitative and qualitative evaluations for a variety of image fusion
missions, including remote sensing pansharpening and hyperspectral image
super-resolution (HISR)
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