Target Detection Method for Low-Resolution Remote Sensing Image Based on ESRGAN and ReDet

Yuwu Wang,Shengwei Guo,Guobing Sun

doi:10.3390/photonics8100431

Yuwu Wang, Shengwei Guo + Show 1 more

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https://doi.org/10.3390/photonics8100431

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Journal: Photonics	Publication Date: Oct 8, 2021
Citations: 4	License type: CC BY 4.0

Affiliation: Heilongjiang University

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 conditions. 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) algorithm, 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.

Highlights

IntroductionThe task of target detection in remote sensing images is to locate, recognize, or classify ground objects
Low-Resolution Remote SensingThe task of target detection in remote sensing images is to locate, recognize, or classify ground objects
The experiments in this paper prove that the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is very suitable for the super-resolution reconstruction of remote sensing images

Summary

Introduction

The task of target detection in remote sensing images is to locate, recognize, or classify ground objects. The task of single image super-resolution (SISR) [4] processing is to recover a high-resolution image from a low-resolution image. Before the deep learning method was proposed, the Bicubic [4] method was usually used to deal with the problem of single image super-resolution. This method only used the pixel information of the low-resolution image itself, and all the pixels at each position were interpolated based on the information around the corresponding pixels so that the super-resolution image obtained by this method was unsatisfactory and had poor image quality.

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