A lightweight distillation CNN-transformer architecture for remote sensing image super-resolution

Abstract

Remote sensing images exhibit rich texture features and strong autocorrelation. Although the super-resolution (SR) method of remote sensing images based on convolutional neural networks (CNN) can capture rich local information, the limited perceptual field prevents it from establishing long-distance dependence on global information, leading to the low accuracy of remote sensing image reconstruction. Furthermore, it is difficult for existing SR methods to be deployed in mobile devices due to their large network parameters and high computational demand. In this study, we propose a lightweight distillation CNN-Transformer SR architecture, named DCTA, for remote sensing SR, addressing the aforementioned issues. Specifically, the proposed DCTA first extracts the coarse features through the coarse feature extraction layer and then learns the deep features of remote sensing at different scales by fusing the feature distillation extraction module of CNN and Transformer. In addition, we introduce the feature fusion module at the end of the feature distillation extraction module to control the information propagation, aiming to select the informative components for better feature fusion. The extracted low-resolution (LR) feature maps are reorganized through the up-sampling module to obtain high-resolution (HR) feature maps with high accuracy to generate high-quality HR remote sensing images. The experiments comparing different methods demonstrate that the proposed approach performs well on multiple datasets, including NWPU-RESISC45, Draper, and UC Merced. This is achieved by balancing reconstruction performance and network complexity, resulting in both competitive subjective and objective results.