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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Gespeichert in:

Autor*in:	Massimiliano Gargiulo [verfasserIn] Antonio Mazza [verfasserIn] Raffaele Gaetano [verfasserIn] Giuseppe Ruello [verfasserIn] Giuseppe Scarpa [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	pansharpening data fusion convolutional neural network multi-resolution analysis landcover classification

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 11(2019), 22, p 2635
Übergeordnetes Werk:	volume:11 ; year:2019 ; number:22, p 2635

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs11222635

Katalog-ID:	DOAJ018054358

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allfieldsSound	10.3390/rs11222635 doi (DE-627)DOAJ018054358 (DE-599)DOAJ30209dcc5f07499fb44ad60199af7a6b DE-627 ger DE-627 rakwb eng Massimiliano Gargiulo verfasserin aut Fast Super-Resolution of 20 m Sentinel-2 Bands Using Convolutional Neural Networks 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. pansharpening data fusion convolutional neural network multi-resolution analysis landcover classification Science Q Antonio Mazza verfasserin aut Raffaele Gaetano verfasserin aut Giuseppe Ruello verfasserin aut Giuseppe Scarpa verfasserin aut In Remote Sensing MDPI AG, 2009 11(2019), 22, p 2635 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:11 year:2019 number:22, p 2635 https://doi.org/10.3390/rs11222635 kostenfrei https://doaj.org/article/30209dcc5f07499fb44ad60199af7a6b kostenfrei https://www.mdpi.com/2072-4292/11/22/2635 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 11 2019 22, p 2635
language	English
source	In Remote Sensing 11(2019), 22, p 2635 volume:11 year:2019 number:22, p 2635
sourceStr	In Remote Sensing 11(2019), 22, p 2635 volume:11 year:2019 number:22, p 2635
format_phy_str_mv	Article
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topic_facet	pansharpening data fusion convolutional neural network multi-resolution analysis landcover classification Science Q
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container_title	Remote Sensing
authorswithroles_txt_mv	Massimiliano Gargiulo @@aut@@ Antonio Mazza @@aut@@ Raffaele Gaetano @@aut@@ Giuseppe Ruello @@aut@@ Giuseppe Scarpa @@aut@@
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author	Massimiliano Gargiulo
spellingShingle	Massimiliano Gargiulo misc pansharpening misc data fusion misc convolutional neural network misc multi-resolution analysis misc landcover classification misc Science misc Q Fast Super-Resolution of 20 m Sentinel-2 Bands Using Convolutional Neural Networks
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topic_title	Fast Super-Resolution of 20 m Sentinel-2 Bands Using Convolutional Neural Networks pansharpening data fusion convolutional neural network multi-resolution analysis landcover classification
topic	misc pansharpening misc data fusion misc convolutional neural network misc multi-resolution analysis misc landcover classification misc Science misc Q
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title	Fast Super-Resolution of 20 m Sentinel-2 Bands Using Convolutional Neural Networks
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title_sort	fast super-resolution of 20 m sentinel-2 bands using convolutional neural networks
title_auth	Fast Super-Resolution of 20 m Sentinel-2 Bands Using Convolutional Neural Networks
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Fast Super-Resolution of 20 m Sentinel-2 Bands Using Convolutional Neural Networks
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