Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution With Subpixel Fusion

Enormous efforts have been recently made to super-resolve hyperspectral (HS) images with the aid of high spatial resolution multispectral (MS) images. Most prior works usually perform the fusion task by means of multifarious pixel-level priors. Yet the intrinsic effects of a large distribution gap between HS-MS data due to differences in the spatial and spectral resolution are less investigated. The gap might be caused by unknown sensor-specific properties or highly-mixed spectral information within one pixel (due to low spatial resolution). To this end, we propose a subpixel-level HS super-resolution framework by devising a novel decoupled-and-coupled network, called DC-Net, to progressively fuse HS-MS…

• NN
  National Natural Science Foundation of China

  Awards: 42271350, 62201553, 42241109
• AR
  AXA Research Fund
• NK
  National Key Research and Development Program of China

  Award: 2022YFB3903401