COMPRESSED HYPERSPECTRAL PANSHARPENING

Hyperspectral (HS) imaging based on compressed sensing (CS) is actively studied to capture an HS image in one shot. Although CS can reconstruct an HS image from a much less number of random observations, capturing an HS image of high spatial and spectral resolution (HR-HS image) is still difficult because of current imaging systems. In this paper, we propose a new methodology of HS imaging, named compressed HS pansharpening. Specifically, the concept enables to generate an HR-HS image from a compressively-sensed observation with the help of a panchromatic (PAN) image. For a realistic setting, the concept assumes that both a CS observation and a PAN image are contaminated by noise. Then, an HR-HS and a clean PAN image are simultaneously estimated from the noisy pair by solving a newly-formulated optimization problem. In the experiments, we demonstrate the utility of our proposed methodology.