Target detection method for low-resolution remote sensing image based on ESRGAN and ReDet

Abstract

With the widespread use of remote sensing images, low-resolution target detection in remote sensing images has become a hot research topic in the field of computer vision. In this paper, we propose a Target Detection on Super-Resolution Reconstruction (TDoSR) method to solve the problem of low target recognition rates in low-resolution remote sensing images under foggy con-ditions. The TDoSR method uses the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to perform defogging and super-resolution reconstruction of foggy low-resolution remote sensing images. In the target detection part, the Rotation Equivariant Detector (ReDet) algo-rithm, which has a higher recognition rate at this stage, is used to identify and classify various types of targets. While a large number of experiments have been carried out on the remote sensing image dataset DOTA-v1.5, the results of this paper suggest that the proposed method achieves good results in the target detection of low-resolution foggy remote sensing images. The principal result of this paper demonstrates that the recognition rate of the TDoSR method increases by roughly 20% when compared with low-resolution foggy remote sensing images.