Super-resolution for Sentinel-2 images

Galar Idoate, Mikel; Sesma Redín, Rubén; Ayala Lauroba, Christian; Aranda, Carlos

Super-resolution for Sentinel-2 images

dc.contributor.author	Galar Idoate, Mikel
dc.contributor.author	Sesma Redín, Rubén
dc.contributor.author	Ayala Lauroba, Christian
dc.contributor.author	Aranda, Carlos
dc.contributor.department	Institute of Smart Cities - ISC	en
dc.date.accessioned	2020-05-19T06:57:57Z
dc.date.available	2020-05-19T06:57:57Z
dc.date.issued	2019
dc.description	Trabajo presentado al PIA19+MRSS19 – Photogrammetric Image Analysis & Munich Remote Sensing Symposium, 2019, Munich	es_ES
dc.description	Incluye póster	es
dc.description.abstract	Obtaining Sentinel-2 imagery of higher spatial resolution than the native bands while ensuring that output imagery preserves the original radiometry has become a key issue since the deployment of Sentinel-2 satellites. Several studies have been carried out on the upsampling of 20m and 60m Sentinel-2 bands to 10 meters resolution taking advantage of 10m bands. However, how to super-resolve 10m bands to higher resolutions is still an open problem. Recently, deep learning-based techniques has become a de facto standard for single-image super-resolution. The problem is that neural network learning for super-resolution requires image pairs at both the original resolution (10m in Sentinel-2) and the target resolution (e.g., 5m or 2.5m). Since there is no way to obtain higher resolution images for Sentinel-2, we propose to consider images from others sensors having the greatest similarity in terms of spectral bands, which will be appropriately pre-processed. These images, together with Sentinel-2 images, will form our training set. We carry out several experiments using state-of-the-art Convolutional Neural Networks for single-image super-resolution showing that this methodology is a first step toward greater spatial resolution of Sentinel-2 images.	en
dc.format.extent	8 p.
dc.format.mimetype	application/pdf	en
dc.identifier.doi	10.5194/isprs-archives-XLII-2-W16-95-2019
dc.identifier.issn	1682-1750
dc.identifier.uri	https://academica-e.unavarra.es/handle/2454/36919
dc.language.iso	eng	en
dc.publisher	International Society for Photogrammetry and Remote Sensing	en
dc.relation.ispartof	International Archives of The Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 2019, XLII-2/W16, 95-102	en
dc.relation.publisherversion	https://doi.org/10.5194/isprs-archives-XLII-2-W16-95-2019
dc.rights	© Authors 2019. Creative Commons Attribution 4.0 International (CC BY 4.0)	en
dc.rights.accessRights	info:eu-repo/semantics/openAccess
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/
dc.subject	Super-resolution	en
dc.subject	Deep learning	en
dc.subject	Sentinel-2	en
dc.subject	Image enhancement	en
dc.subject	Convolutional neural network	en
dc.subject	Optical images	en
dc.title	Super-resolution for Sentinel-2 images	en
dc.type	info:eu-repo/semantics/conferenceObject
dc.type.version	info:eu-repo/semantics/publishedVersion
dspace.entity.type	Publication
relation.isAuthorOfPublication	44c7a308-9c21-49ef-aa03-b45c2c5a06fd
relation.isAuthorOfPublication	806651ab-0bba-4f13-9b29-2109c4b8b72a
relation.isAuthorOfPublication	4c0a0a12-02e3-479d-8562-b5d9a39bab40
relation.isAuthorOfPublication.latestForDiscovery	44c7a308-9c21-49ef-aa03-b45c2c5a06fd