  Pansharpening enhances spatial details of high spectral resolution
multispectral images using features of high spatial resolution panchromatic
image. There are a number of traditional pansharpening approaches but producing
an image exhibiting high spectral and spatial fidelity is still an open
problem. Recently, deep learning has been used to produce promising
pansharpened images; however, most of these approaches apply similar treatment
to both multispectral and panchromatic images by using the same network for
feature extraction. In this work, we present present a novel dual
attention-based two-stream network. It starts with feature extraction using two
separate networks for both images, an encoder with attention mechanism to
recalibrate the extracted features. This is followed by fusion of the features
forming a compact representation fed into an image reconstruction network to
produce a pansharpened image. The experimental results on the Pl\'{e}iades
dataset using standard quantitative evaluation metrics and visual inspection
demonstrates that the proposed approach performs better than other approaches
in terms of pansharpened image quality.
