An improved super-resolution method for remote sensing based on generative adversarial network

This paper proposes an improved GAN-based super-resolution (SR) network to address the issues of detail loss and low feature utilization in remote sensing image SR reconstruction. First, we propose a residual block (ERDB) containing multi-scale receptive fields (MRFs) to fully capture features at different scales, and use the attention module to dynamically adjust the features. In the discriminator network, we use the relative discriminator to compute the relative probability instead of the absolute probability and incorporate an attention module to help generate images containing more texture details. Experimental results demonstrate that our method has improved objective evaluation metrics on both UC Merced and NWPU-RESISC4 datasets and achieved good visual effects.