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  • 标题:ENHANCED SUPER RESOLUTION FOR REMOTE SENSING IMAGERIES
  • 本地全文:下载
  • 作者:R. G. C. J. Kapilaratne ; S. Kakuta ; S. Kaneta
  • 期刊名称:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
  • 印刷版ISSN:2194-9042
  • 电子版ISSN:2194-9050
  • 出版年度:2022
  • 卷号:V-3-2022
  • 页码:53-60
  • DOI:10.5194/isprs-annals-V-3-2022-53-2022
  • 语种:English
  • 出版社:Copernicus Publications
  • 摘要:Single image super resolution (SISR) technology has been attracted much attention from remote sensing community due to its proven potentials in remote sensing applications. Existing SISR techniques varying from conventional interpolation methods to different network architectures. Generative adversarial networks (GANs) are one of the latest network architectures proven a greater potential as a SISR method whereas least attention has been given by the remote sensing community. Several studies have already been carried out on this context. However, yet there is no generalized GAN based approach to super resolve remote sensing imageries. Therefore, this study investigated the potentials of enhanced super resolution generative adversarial (ESRGAN) model to super resolve very high to medium resolution images from high to coarse resolution images for remote sensing applications. Two models were trained and Worldview-3 (WV3) images used as for very high resolution images. Whereas, down sampled WV3 and Sentinel-2(S2) were used as low resolution counterparts. Model performances were qualitatively and quantitatively analysed using standard metrics such as PSNR, SSIM, UIQI, CC, SAM, SID. Evaluation results emphasised super resolved images were preserved the original quality of the satellite images to a greater extent while improving its ground resolution.
  • 关键词:Remote Sensing; Super Resolution; ESRGAN; Deep Learning; WorldView-3; Sentinel-2
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