  Image super-resolution (SR) methods can generate remote sensing images with
high spatial resolution without increasing the cost, thereby providing a
feasible way to acquire high-resolution remote sensing images, which are
difficult to obtain due to the high cost of acquisition equipment and complex
weather. Clearly, image super-resolution is a severe ill-posed problem.
Fortunately, with the development of deep learning, the powerful fitting
ability of deep neural networks has solved this problem to some extent. In this
paper, we propose a network based on the generative adversarial network (GAN)
to generate high resolution remote sensing images, named the multi-attention
generative adversarial network (MA-GAN). We first designed a GAN-based
framework for the image SR task. The core to accomplishing the SR task is the
image generator with post-upsampling that we designed. The main body of the
generator contains two blocks; one is the pyramidal convolution in the
residual-dense block (PCRDB), and the other is the attention-based upsample
(AUP) block. The attentioned pyramidal convolution (AttPConv) in the PCRDB
block is a module that combines multi-scale convolution and channel attention
to automatically learn and adjust the scaling of the residuals for better
results. The AUP block is a module that combines pixel attention (PA) to
perform arbitrary multiples of upsampling. These two blocks work together to
help generate better quality images. For the loss function, we design a loss
function based on pixel loss and introduce both adversarial loss and feature
loss to guide the generator learning. We have compared our method with several
state-of-the-art methods on a remote sensing scene image dataset, and the
experimental results consistently demonstrate the effectiveness of the proposed
MA-GAN.
