Super-resolution for Sentinel-2 images
Fecha
2019Versión
Acceso abierto / Sarbide irekia
Tipo
Contribución a congreso / Biltzarrerako ekarpena
Versión
Versión publicada / Argitaratu den bertsioa
Impacto
|
10.5194/isprs-archives-XLII-2-W16-95-2019
Resumen
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 ban ...
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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. [--]
Materias
Super-resolution,
Deep learning,
Sentinel-2,
Image enhancement,
Convolutional neural network,
Optical images
Editor
International Society for Photogrammetry and Remote Sensing
Publicado en
International Archives of The Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 2019, XLII-2/W16, 95-102
Notas
Trabajo presentado al PIA19+MRSS19 – Photogrammetric Image Analysis & Munich Remote Sensing Symposium, 2019, MunichIncluye póster
Departamento
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute of Smart Cities - ISC