  This paper presents a deep learning-based estimation of the intensity
component of MultiSpectral bands by considering joint multiplication of the
neighbouring spectral bands. This estimation is conducted as part of the
component substitution approach for fusion of PANchromatic and MultiSpectral
images in remote sensing. After computing the band dependent intensity
components, a deep neural network is trained to learn the nonlinear
relationship between a PAN image and its nonlinear intensity components. Low
Resolution MultiSpectral bands are then fed into the trained network to obtain
an estimate of High Resolution MultiSpectral bands. Experiments conducted on
three datasets show that the developed deep learning-based estimation approach
provides improved performance compared to the existing methods based on three
objective metrics.
