Computer Science ›› 2022, Vol. 49 ›› Issue (2): 116-122.doi: 10.11896/jsjkx.210700095

• Computer Vision: Theory and Application • Previous Articles     Next Articles

Study on Super-resolution Reconstruction Algorithm of Remote Sensing Images in Natural Scene

CHEN Gui-qiang, HE Jun   

  1. College of Computer Science,Sichuan University,Chengdu 610065,China
  • Received:2021-07-09 Revised:2021-10-15 Online:2022-02-15 Published:2022-02-23
  • About author:CHEN Gui-qiang,born in 1990,postgraduate,is a member of China Computer Federation.His main research interests include deep learning and computer vision.
    HE Jun,born in 1970,Ph.D,vice professor,postgraduate supervisor.His main research interests include computer network and intelligent control system.
  • Supported by:
    National Nature Science Foundation of China(U1836103) and Key Research and Development Projects of Science and Technology of Sichuan Province(18ZDYF2039).

Abstract: Due to the lack of paired datasets in the field of remote sensing image super-resolution reconstruction,current methods obtain low resolution images by bicubic interpolation,in which the degradation model is too idealized,resulting in unsatisfied reconstruction results in real low resolution remote sensing images situations.This paper proposes a super resolution reconstruction algorithm for real remote sensing images.For datasets that lack paired images,this paper builds a more reasonable degradation model,in which a prior of degradation in the imaging process (like blur,noise,down sampling,etc.) is randomly shuffled to generate realistic low-resolution images for training,simulating the generation process of low-resolution remote sensing images.Also,this paper improves a reconstruction algorithm based on generative adversarial networks(GAN) to enhance texture details by introducing attention mechanism.Experiments on UC Merced dataset show a promotion of 1.407 1 dB/0.067 2,0.821 1 dB/0.023 5 compared with ESRGAN and RCAN on the evaluation index of PSNR/SSIM,experiments on Alsat2B dataset promote 1.758 4 dB/0.048 5 compared with the baseline,which show the effective of the degradation model and reconstruction architecture.

Key words: Attention mechanism, Degradation, Generative adversarial network, Remote sensing, Super-resolution reconstruction

CLC Number: 

  • TP399
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