Sentinel-3 Super-Resolution Based on Dense Multireceptive Channel Attention
Ver/ Abrir
Impacto
Scholar |
Otros documentos de la autoría: Fernández, Rafael; Fernandez-Beltran, Ruben; kang, jian; Pla, Filiberto
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
Sentinel-3 Super-Resolution Based on Dense Multireceptive Channel AttentionFecha de publicación
2021-07-16Editor
IEEEISSN
1939-1404; 2151-1535Cita bibliográfica
R. Fernandez, R. Fernandez-Beltran, J. Kang and F. Pla, "Sentinel-3 Super-Resolution Based on Dense Multireceptive Channel Attention," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 7359-7372, 2021, doi: 10.1109/JSTARS.2021.3097410.Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
The unprecedented availability of remote sensing data
from different complementary Sentinel missions provides increasing opportunities to alleviate the spatial limitations of Sentinel-3
(S3) from an intersensor ... [+]
The unprecedented availability of remote sensing data
from different complementary Sentinel missions provides increasing opportunities to alleviate the spatial limitations of Sentinel-3
(S3) from an intersensor perspective. Nonetheless, effectively exploiting such intersensor synergies still raises important challenges
for super-resolution (SR) algorithms in terms of operational data
availability, sensor alignment and substantial resolution changes,
among others. In this scenario, this article sets a new SR framework
for spatially enhancing S3 ocean and land color instrument (OLCI)
products by taking advantage of the higher spatial resolution of
the Sentinel-2 (S2) multispectral instrument (MSI). To achieve
this goal, we initially study some of the most important deep
learning-based approaches. Then, we define a novel Level-4 SR
framework which integrates a new convolutional neural network
specially designed for super-resolving OLCI data. In contrast to
other networks, the proposed SR architecture (termed as SRS3)
employs a dense multireceptive field together with a residual channel attention mechanism to relieve the particularly low spatial
resolution of OLCI while extracting more discriminating features
for the large spatial resolution differences with respect to MSI. The
experimental part of the work, conducted using ten coupled OLCI
and MSI operational data, reveals the suitability of the presented
Level-4 SR framework within the Copernicus programme context
as well as the advantages of the proposed architecture with respect different state-of-the-art models when spatially enhancing
OLCI products. The related codes will be publicly available at
https://github.com/rufernan/SRS3. [-]
Publicado en
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 14, 2021Entidad financiadora
Ministerio de Ciencia, Innovación y Universidades (España) | Generalitat Valenciana
Código del proyecto o subvención
RTI2018-098651-B-C54 | GV/2020/167
Proyecto de investigación
Productos avanzados L3 y L4 para la misión FLEX-S3 (FLEXL3L4)Fusión de datos multi-sensor y temporales para la misión espacial FLEX
Derechos de acceso
info:eu-repo/semantics/openAccess
Aparece en las colecciones
- INIT_Articles [749]