Fast Super-Resolution of 20 m Sentinel-2 Bands Using Convolutional Neural Networks
Images provided by the ESA Sentinel-2 mission are rapidly becoming the main source of information for the entire remote sensing community, thanks to their unprecedented combination of spatial, spectral and temporal resolution, as well as their associated open access policy. Due to a sensor design tr...
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doaj-30209dcc5f07499fb44ad60199af7a6b2020-11-25T00:04:25ZengMDPI AGRemote Sensing2072-42922019-11-011122263510.3390/rs11222635rs11222635Fast Super-Resolution of 20 m Sentinel-2 Bands Using Convolutional Neural NetworksMassimiliano Gargiulo0Antonio Mazza1Raffaele Gaetano2Giuseppe Ruello3Giuseppe Scarpa4Department of Electrical Engineering and Information Technology (DIETI), University Federico II, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology (DIETI), University Federico II, 80125 Naples, ItalyCentre International de Recherche Agronomique pour le Développement (CIRAD), Unité Mixte de Recherche Territoires, Environnement, Télédétéction et Information Spatiale (UMR TETIS), Maison de la Télédétéction, 34000 Montpellier, FranceDepartment of Electrical Engineering and Information Technology (DIETI), University Federico II, 80125 Naples, ItalyDepartment of Electrical Engineering and Information Technology (DIETI), University Federico II, 80125 Naples, ItalyImages provided by the ESA Sentinel-2 mission are rapidly becoming the main source of information for the entire remote sensing community, thanks to their unprecedented combination of spatial, spectral and temporal resolution, as well as their associated open access policy. Due to a sensor design trade-off, images are acquired (and delivered) at different spatial resolutions (10, 20 and 60 m) according to specific sets of wavelengths, with only the four visible and near infrared bands provided at the highest resolution (10 m). Although this is not a limiting factor in general, many applications seem to emerge in which the resolution enhancement of 20 m bands may be beneficial, motivating the development of specific super-resolution methods. In this work, we propose to leverage Convolutional Neural Networks (CNNs) to provide a fast, upscalable method for the single-sensor fusion of Sentinel-2 (S2) data, whose aim is to provide a 10 m super-resolution of the original 20 m bands. Experimental results demonstrate that the proposed solution can achieve better performance with respect to most of the state-of-the-art methods, including other deep learning based ones with a considerable saving of computational burden.https://www.mdpi.com/2072-4292/11/22/2635pansharpeningdata fusionconvolutional neural networkmulti-resolution analysislandcover classification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Massimiliano Gargiulo Antonio Mazza Raffaele Gaetano Giuseppe Ruello Giuseppe Scarpa |
spellingShingle |
Massimiliano Gargiulo Antonio Mazza Raffaele Gaetano Giuseppe Ruello Giuseppe Scarpa Fast Super-Resolution of 20 m Sentinel-2 Bands Using Convolutional Neural Networks Remote Sensing pansharpening data fusion convolutional neural network multi-resolution analysis landcover classification |
author_facet |
Massimiliano Gargiulo Antonio Mazza Raffaele Gaetano Giuseppe Ruello Giuseppe Scarpa |
author_sort |
Massimiliano Gargiulo |
title |
Fast Super-Resolution of 20 m Sentinel-2 Bands Using Convolutional Neural Networks |
title_short |
Fast Super-Resolution of 20 m Sentinel-2 Bands Using Convolutional Neural Networks |
title_full |
Fast Super-Resolution of 20 m Sentinel-2 Bands Using Convolutional Neural Networks |
title_fullStr |
Fast Super-Resolution of 20 m Sentinel-2 Bands Using Convolutional Neural Networks |
title_full_unstemmed |
Fast Super-Resolution of 20 m Sentinel-2 Bands Using Convolutional Neural Networks |
title_sort |
fast super-resolution of 20 m sentinel-2 bands using convolutional neural networks |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2019-11-01 |
description |
Images provided by the ESA Sentinel-2 mission are rapidly becoming the main source of information for the entire remote sensing community, thanks to their unprecedented combination of spatial, spectral and temporal resolution, as well as their associated open access policy. Due to a sensor design trade-off, images are acquired (and delivered) at different spatial resolutions (10, 20 and 60 m) according to specific sets of wavelengths, with only the four visible and near infrared bands provided at the highest resolution (10 m). Although this is not a limiting factor in general, many applications seem to emerge in which the resolution enhancement of 20 m bands may be beneficial, motivating the development of specific super-resolution methods. In this work, we propose to leverage Convolutional Neural Networks (CNNs) to provide a fast, upscalable method for the single-sensor fusion of Sentinel-2 (S2) data, whose aim is to provide a 10 m super-resolution of the original 20 m bands. Experimental results demonstrate that the proposed solution can achieve better performance with respect to most of the state-of-the-art methods, including other deep learning based ones with a considerable saving of computational burden. |
topic |
pansharpening data fusion convolutional neural network multi-resolution analysis landcover classification |
url |
https://www.mdpi.com/2072-4292/11/22/2635 |
work_keys_str_mv |
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