Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/75355
Title: Super-resolution of Sentinel-2 imagery using generative adversarial networks
Authors: Romero, Luis Salgueiro
Marcello, Javier 
Vilaplana, Verónica
UNESCO Clasification: 250616 Teledetección (Geología)
Keywords: Deep Learning
Generative Adversarial Network
Sentinel-2
Super-Resolution
Worldview
Issue Date: 2020
Journal: Remote Sensing 
Abstract: Sentinel-2 satellites provide multi-spectral optical remote sensing images with four bands at 10 m of spatial resolution. These images, due to the open data distribution policy, are becoming an important resource for several applications. However, for small scale studies, the spatial detail of these images might not be sufficient. On the other hand, WorldView commercial satellites offer multi-spectral images with a very high spatial resolution, typically less than 2 m, but their use can be impractical for large areas or multi-temporal analysis due to their high cost. To exploit the free availability of Sentinel imagery, it is worth considering deep learning techniques for single-image super-resolution tasks, allowing the spatial enhancement of low-resolution (LR) images by recovering high-frequency details to produce high-resolution (HR) super-resolved images. In this work, we implement and train a model based on the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) with pairs of WorldView-Sentinel images to generate a super-resolved multispectral Sentinel-2 output with a scaling factor of 5. Our model, named RS-ESRGAN, removes the upsampling layers of the network to make it feasible to train with co-registered remote sensing images. Results obtained outperform state-of-the-art models using standard metrics like PSNR, SSIM, ERGAS, SAM and CC. Moreover, qualitative visual analysis shows spatial improvements as well as the preservation of the spectral information, allowing the super-resolved Sentinel-2 imagery to be used in studies requiring very high spatial resolution.
URI: http://hdl.handle.net/10553/75355
ISSN: 2072-4292
DOI: 10.3390/RS12152424
Source: Remote Sensing[EISSN 2072-4292],v. 12 (15), (Agosto 2020)
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