  In recent years, convolutional neural networks (CNNs) have achieved
remarkable advancement in the field of remote sensing image super-resolution
due to the complexity and variability of textures and structures in remote
sensing images (RSIs), which often repeat in the same images but differ across
others. Current deep learning-based super-resolution models focus less on
high-frequency features, which leads to suboptimal performance in capturing
contours, textures, and spatial information. State-of-the-art CNN-based methods
now focus on the feature extraction of RSIs using attention mechanisms.
However, these methods are still incapable of effectively identifying and
utilizing key content attention signals in RSIs. To solve this problem, we
proposed an advanced feature extraction module called Channel and Spatial
Attention Feature Extraction (CSA-FE) for effectively extracting the features
by using the channel and spatial attention incorporated with the standard
vision transformer (ViT). The proposed method trained over the UCMerced dataset
on scales 2, 3, and 4. The experimental results show that our proposed method
helps the model focus on the specific channels and spatial locations containing
high-frequency information so that the model can focus on relevant features and
suppress irrelevant ones, which enhances the quality of super-resolved images.
Our model achieved superior performance compared to various existing models.
