ViT-ISRGAN: A High-Quality Super-Resolution Reconstruction Method for Multispectral Remote Sensing Images

Abstract

The reflective characteristics of remote sensing image information depend on the scale of the observed area, with high-resolution images providing more detailed feature information. Currently, monitoring refined industries and extracting regional information necessitate higher-resolution remote sensing images. Super-resolution reconstruction of remote sensing multispectral images not only enhances the spatial resolution of these images but also preserves and improves the spectral information of multispectral data, thereby providing richer ground object information and more accurate environmental monitoring data. To improve the effectiveness of feature extraction in the generator network while maintaining model efficiency, this article proposes the vision transformer improved super-resolution generative adversarial network (ViT-ISRGAN) model. This model is an improvement upon the original SRGAN super-resolution image reconstruction method, incorporating lightweight network modules, channel attention modules, spatial-spectral residual attention, and the vision transformer structure. The ViT-ISRGAN model focuses on reconstructing four types of typical ground objects based on Sentinel-2 images: urban, water, farmland, and forest. Results indicate that the ViT-ISRGAN model excels in capturing texture details and color restoration, effectively extracting spectral and texture information from multispectral remote sensing images across various scenes. Compared to other super-resolution (SR) models, this approach demonstrates superior effectiveness and performance in the SR tasks of remote sensing multispectral images.