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This paper presents a deep learning-based estimation of the intensitycomponent of MultiSpectral bands by considering joint multiplication of theneighbouring spectral bands. This estimation is conducted as part of thecomponent substitution approach for fusion of PANchromatic and MultiSpectralimages in remote sensing. After computing the band dependent intensitycomponents, a deep neural network is trained to learn the nonlinearrelationship between a PAN image and its nonlinear intensity components. LowResolution MultiSpectral bands are then fed into the trained network to obtainan estimate of High Resolution MultiSpectral bands. Experiments conducted onthree datasets show that the developed deep learning-based estimation approachprovides improved performance compared to the existing methods based on threeobjective metrics.

Deep Learning-Based Detail Map Estimation for MultiSpectral Image Fusion in Remote Sensing