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Title: A generative adversarial network approach for super resolution of sentinel-2 satellite images
Advisor(s): Beltrán Castañón, César Armando
OCDE field: https://purl.org/pe-repo/ocde/ford#1.02.00
Issue Date: 17-Mar-2020
Institution: Pontificia Universidad Católica del Perú
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.
Discipline: Informática con mención en Ciencias de la Computación
Grade or title grantor: Pontificia Universidad Católica del Perú. Escuela de Posgrado
Grade or title: Magíster en Informática con mención en Ciencias de la Computación
Register date: 18-Mar-2020; 18-Mar-2020



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