Application of deep learning models in nonlinear detail map prediction in pansharpening

Abstract This paper provides a deep learning-based approximation of the MultiSpectral Band Intensity component by considering the joint multiplication of adjacent spectral channels. This calculation is conducted as part of a component substitution approach for the fusion of PANchromatic and MultiSpectral images in remote sensing. After calculating the band-dependent intensity elements, a deep learning model is trained to learn the nonlinear relationship between the PAN image and its nonlinear intensity elements. Low Resolution MultiSpectral bands are then fed into a trained network to achieve a high resolution MultiSpectral band estimation. Experiments performed on three datasets indicate that the established deep learning estimation methodology offers better performance compared to current approaches based on a number of objective metrics.