Image super-resolution (SR) methods can generate remote sensing images with high spatial resolution without increasing the cost of acquisition equipment, thereby providing a feasible way to improve the quality of remote sensing images. Clearly, image SR is a severe ill-posed problem. With the development of deep learning, the powerful fitting ability of deep neural networks has solved this problem to some extent. Since the texture information of various remote sensing images are totally different from each other, in this article, we proposed a network based on generative adversarial network (GAN) to achieve high-resolution remote sensing images, named multiattention GAN (MA-GAN). The main body of the generator in MA-GAN contains three blocks: pyramid convolutional residual dense (PCRD) block, attention-based upsampling (AUP) block, and attention-based fusion (AF) block. Specifically, the developed attention pyramid convolutional (AttPConv) operator in the PCRD block combines multiscale convolution and channel attention (CA) to automatically learn and adjust the scale of residuals for better representation. The established AUP block uses pixel attention (PA) to perform arbitrary scales of upsampling. The AF block uses branch attention (BA) to integrate upsampled low-resolution images with high-level features. Besides, the loss function takes both adversarial loss and feature loss into consideration to guide the learning procedure of the generator. We have compared our MA-GAN approach with several state-of-the-art methods on a number of remote sensing scenes, and the experimental results consistently demonstrate the effectiveness of the proposed MA-GAN. For study replication, the source code will be released at: https://github.com/ZhihaoWang1997/MA-GAN.

中文翻译：

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用于遥感图像超分辨率的多注意生成对抗网络

图像超分辨率（SR）方法可以在不增加采集设备成本的情况下生成具有高空间分辨率的遥感图像，从而为提高遥感图像质量提供了一条可行的途径。显然，图像 SR 是一个严重的不适定问题。随着深度学习的发展，深度神经网络强大的拟合能力在一定程度上解决了这个问题。由于各种遥感图像的纹理信息完全不同，在本文中，我们提出了一种基于生成对抗网络（GAN）来实现高分辨率遥感图像的网络，称为多注意力GAN（MA-GAN）。MA-GAN 中生成器的主体包含三个块：金字塔卷积残差密集（PCRD）块、基于注意力的上采样（AUP）块、和基于注意力的融合（AF）块。具体来说，PCRD 块中开发的注意力金字塔卷积 (AttPConv) 算子结合了多尺度卷积和通道注意力 (CA) 来自动学习和调整残差的尺度以获得更好的表示。已建立的 AUP 块使用像素注意 (PA) 来执行任意尺度的上采样。AF 块使用分支注意 (BA) 将上采样的低分辨率图像与高级特征集成。此外，损失函数同时考虑了对抗性损失和特征损失来指导生成器的学习过程。我们在多个遥感场景上将我们的 MA-GAN 方法与几种最先进的方法进行了比较，实验结果一致地证明了所提出的 MA-GAN 的有效性。对于研究复制，https://github.com/ZhihaoWang1997/MA-GAN.