Published Paper Details:

Pan-sharpening combines multidimensional and panchromatic satellite images to generate higher-resolution products. This research compares standard component replacement and index injection approaches to current deep learning methods such as convolutional and adversarial generative networks. A comparative examination utilizing spatial/spectral quality criteria and visual evaluation demonstrates that deep learning algorithms enhance color and feature retention, despite the greater comput- ing cost. Traditional methods successfully brighten features, but they exhibit greater spectrum aberrations. Overall, this trade-off analysis, informed by quantitative and qualitative performance parameters, aids in the appropriate selection of pan-sharpening paradigms adapted to application goals for high fidelity fused Earth observation data. Deep learning has enormous potential for enhancing the state-of-the-art if spectral integrity is prioritized above spatial sharpness alone.