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Pansharpening enhances spatial details of high spectral resolutionmultispectral images using features of high spatial resolution panchromaticimage. There are a number of traditional pansharpening approaches but producingan image exhibiting high spectral and spatial fidelity is still an openproblem. Recently, deep learning has been used to produce promisingpansharpened images; however, most of these approaches apply similar treatmentto both multispectral and panchromatic images by using the same network forfeature extraction. In this work, we present present a novel dualattention-based two-stream network. It starts with feature extraction using twoseparate networks for both images, an encoder with attention mechanism torecalibrate the extracted features. This is followed by fusion of the featuresforming a compact representation fed into an image reconstruction network toproduce a pansharpened image. The experimental results on the Pl\'{e}iadesdataset using standard quantitative evaluation metrics and visual inspectiondemonstrates that the proposed approach performs better than other approachesin terms of pansharpened image quality.

Deep dual stream residual network with contextual attention for pansharpening of remote sensing images