Pansharpening approach via two-stream detail injection based on relativistic generative adversarial networks
作者：
Highlights：
• We propose a new pan-sharpening technique based on relativistic generative adversarial networks.
• It enhances the spatial quality by injecting the high-frequency detail into the MS image.
• The generative behavior is improved by exploiting a Relativistic average Discriminator.
• An effective loss makes the fusion product more realistic with high spectral fidelity.
• Our technique yields very competitive visual and quantitative fusion results.
摘要
•We propose a new pan-sharpening technique based on relativistic generative adversarial networks.•It enhances the spatial quality by injecting the high-frequency detail into the MS image.•The generative behavior is improved by exploiting a Relativistic average Discriminator.•An effective loss makes the fusion product more realistic with high spectral fidelity.•Our technique yields very competitive visual and quantitative fusion results.
论文关键词：Pansharpening,CNN,Detail injection,Relativistic GAN
论文评审过程：Received 2 June 2021, Revised 17 August 2021, Accepted 28 September 2021, Available online 14 October 2021, Version of Record 20 October 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115996
