Pan-sharpening is an indispensable technology for remote sensing that aims to combine low-resolution multispectral images and high-resolution panchromatic images to create a multispectral image with high resolution. However, pan-sharpening approaches often encounter spectral distortion and detail distortion issues. In order to overcome the drawbacks of pan-sharpening methodologies, we propose an end-to-end pan-sharpening model consisting of an effective generative adversarial network architecture equipped with spatial feature transform layers that generate spatial detail features under spectral feature constraints. Through a large number of quantitative and visual assessments, we demonstrate that the proposed method achieves superior performance to other state-of-the-art methods.
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