Multiattention Generative Adversarial Network for Remote Sensing Image Super-Resolution

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.