Publicación:
Super Resolution Approach Using Generative Adversarial Network Models for Improving Satellite Image Resolution

dc.contributor.author Pineda F. es_PE
dc.contributor.author Ayma V. es_PE
dc.contributor.author Aduviri R. es_PE
dc.contributor.author Beltran C. es_PE
dc.date.accessioned 2024-05-30T23:13:38Z
dc.date.available 2024-05-30T23:13:38Z
dc.date.issued 2020
dc.description.abstract Recently, the number of satellite imaging sensors deployed in space has experienced a considerable increase, but most of these sensors provide low spatial resolution images, and only a small proportion contribute with images at higher resolutions. This work proposes an alternative to improve the spatial resolution of Landsat-8 images to the reference of Sentinel-2 images, by applying a Super Resolution (SR) approach based on the use of Generative Adversarial Network (GAN) models for image processing, as an alternative to traditional methods to achieve higher resolution images, hence, remote sensing applications could take advantage of this new information and improve its outcomes. We used two datasets to train and validate our approach, the first composed by images from the DIV2K open access dataset and the second by images from Sentinel-2 satellite. The experimental results are based on the comparison of the similarity between the Landsat-8 images obtained by the super resolution processing by our approach (for both datasets), against its corresponding reference from Sentinel-2 satellite image, computing the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity (SSIM) as metrics for this purpose. In addition, we present a visual report in order to compare the performance of each trained model, analysis that shows interesting improvements of the resolution of Landsat-8 satellite images. © Springer Nature Switzerland AG 2020.
dc.description.sponsorship Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec
dc.identifier.doi https://doi.org/10.1007/978-3-030-46140-9_27
dc.identifier.scopus 2-s2.0-85084819739
dc.identifier.uri https://hdl.handle.net/20.500.12390/2601
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartof Communications in Computer and Information Science
dc.rights info:eu-repo/semantics/openAccess
dc.subject Super Resolution
dc.subject Landsat-8 es_PE
dc.subject Sentinel-2 es_PE
dc.subject SR-GAN es_PE
dc.subject.ocde http://purl.org/pe-repo/ocde/ford#2.02.04
dc.title Super Resolution Approach Using Generative Adversarial Network Models for Improving Satellite Image Resolution
dc.type info:eu-repo/semantics/article
dspace.entity.type Publication
Archivos