An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution

TL;DR

This paper introduces CSA-FE, a novel feature extraction module combining attention mechanisms and vision transformers to improve remote sensing image super-resolution, focusing on high-frequency details for better image quality.

Contribution

The paper proposes CSA-FE, an advanced feature extraction module that effectively captures high-frequency features in remote sensing images using attention and vision transformers.

Findings

Enhanced super-resolution quality on UCMerced dataset

Superior performance compared to existing models

Effective focus on high-frequency features

Abstract

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.