A Super-resolution Algorithm for Remote Sensing Images based on Data Enhancement and Generative Adversarial Networks

Abstract

In this paper, a new algorithm is proposed to address the problem of excessive errors in current super-resolution algorithms for remote sensing images, which optimizes the processing effect of generative adversarial networks by increasing the type and size of training data with data enhancement techniques. First, the algorithm constructs a more reasonable degradation model for the problem of lacking data sets of paired images, and randomly mixes and washes the degraded prior knowledge (such as blur, noise, downsampling, etc.) in the imaging process to simulate the generation process of low-resolution remote sensing images in natural scenes, and generates realistic low-resolution images for training; meanwhile, the algorithm uses ResNet34 and CNN as the basis to enhance the texture details of remote sensing images, thus richer features can be extracted from the images. The results of experiments conducted on the Alsat2 B real dataset show that the method reduces the time and cost required for sample data acquisition, optimizes the processing speed, enhances the temporal and spatial resolution of remote sensing images, and improves the effect of super-resolution processing.