PC-GANs: Progressive Compensation Generative Adversarial Networks for Pan-sharpening

• URL: http://arxiv.org/abs/2207.14451v1
• Date: Fri, 29 Jul 2022 03:09:21 GMT
• Title: PC-GANs: Progressive Compensation Generative Adversarial Networks for Pan-sharpening
• Authors: Yinghui Xing, Shuyuan Yang, Song Wang, Yan Zhang, Yanning Zhang
• Abstract summary: We propose a novel two-step model for pan-sharpening that sharpens the MS image through the progressive compensation of the spatial and spectral information. The whole model is composed of triple GANs, and based on the specific architecture, a joint compensation loss function is designed to enable the triple GANs to be trained simultaneously.
• Score: 50.943080184828524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The fusion of multispectral and panchromatic images is always dubbed pansharpening. Most of the available deep learning-based pan-sharpening methods sharpen the multispectral images through a one-step scheme, which strongly depends on the reconstruction ability of the network. However, remote sensing images always have large variations, as a result, these one-step methods are vulnerable to the error accumulation and thus incapable of preserving spatial details as well as the spectral information. In this paper, we propose a novel two-step model for pan-sharpening that sharpens the MS image through the progressive compensation of the spatial and spectral information. Firstly, a deep multiscale guided generative adversarial network is used to preliminarily enhance the spatial resolution of the MS image. Starting from the pre-sharpened MS image in the coarse domain, our approach then progressively refines the spatial and spectral residuals over a couple of generative adversarial networks (GANs) that have reverse architectures. The whole model is composed of triple GANs, and based on the specific architecture, a joint compensation loss function is designed to enable the triple GANs to be trained simultaneously. Moreover, the spatial-spectral residual compensation structure proposed in this paper can be extended to other pan-sharpening methods to further enhance their fusion results. Extensive experiments are performed on different datasets and the results demonstrate the effectiveness and efficiency of our proposed method.

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