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    A generative adversarial network approach for super resolution of sentinel-2 satellite images

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    Acceso cerrado
    Date
    2020-03-17
    Author
    Pineda Ancco, Ferdinand Edgardo
    Acceso a Texto completo
    URI
    http://hdl.handle.net/20.500.12404/16137
    Abstract
    Recently, satellites in operation offering very high-resolution (VHR) images has experienced an important increase, but they remain as a smaller proportion against existing lower resolution (HR) satellites. Our work proposes an alternative to improve the spatial resolution of HR images obtained by Sentinel-2 satellite by using the VHR images from PeruSat1, a Peruvian satellite, which serve as the reference for the superresolution approach implementation based on a Generative Adversarial Network (GAN) model, as an alternative for obtaining VHR images. The VHR PeruSat-1 image dataset is used for the training process of the network. The results obtained were analyzed considering the Peak Signal to Noise Ratios (PSNR), the Structural Similarity (SSIM) and the Erreur Relative Globale Adimensionnelle de Synth`ese (ERGAS). Finally, some visual outcomes, over a given testing dataset, are presented so the performance of the model could be analyzed as well.
    Temas
    Satélites artificiales en telecomunicaciones
    Procesamiento de imágenes digitales
    Collections
    • Informática con mención en Ciencias de la Computación

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