  Pansharpening is an essential preprocessing step for remote sensing image
processing. Although deep learning (DL) approaches performed well on this task,
current upsampling methods used in these approaches only utilize the local
information of each pixel in the low-resolution multispectral (LRMS) image
while neglecting to exploit its global information as well as the cross-modal
information of the guiding panchromatic (PAN) image, which limits their
performance improvement. To address this issue, this paper develops a novel
probability-based global cross-modal upsampling (PGCU) method for
pan-sharpening. Precisely, we first formulate the PGCU method from a
probabilistic perspective and then design an efficient network module to
implement it by fully utilizing the information mentioned above while
simultaneously considering the channel specificity. The PGCU module consists of
three blocks, i.e., information extraction (IE), distribution and expectation
estimation (DEE), and fine adjustment (FA). Extensive experiments verify the
superiority of the PGCU method compared with other popular upsampling methods.
Additionally, experiments also show that the PGCU module can help improve the
performance of existing SOTA deep learning pansharpening methods. The codes are
available at https://github.com/Zeyu-Zhu/PGCU.
