DAT-CNN: Dual Attention Temporal CNN for Time-Resolving Sentinel-3 Vegetation Indices
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Otros documentos de la autoría: Ibáñez Fernández, Damián; Fernandez-Beltran, Ruben; Pla, Filiberto; Yokoya, Naoto
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Título
DAT-CNN: Dual Attention Temporal CNN for Time-Resolving Sentinel-3 Vegetation IndicesFecha de publicación
2022-03-22Editor
IEEEISSN
1939-1404; 2151-1535Cita bibliográfica
D. Ibañez, R. Fernandez-Beltran, F. Pla and N. Yokoya, "DAT-CNN: Dual Attention Temporal CNN for Time-Resolving Sentinel-3 Vegetation Indices," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 2632-2643, 2022, doi: 10.1109/JSTARS.2022.3161190.Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
The synergies between Sentinel-3 (S3) and the forthcoming fluorescence explorer (FLEX) mission bring us the opportunity of using S3 vegetation indices (VI) as proxies of the
solar-induced chlorophyll fluorescence ... [+]
The synergies between Sentinel-3 (S3) and the forthcoming fluorescence explorer (FLEX) mission bring us the opportunity of using S3 vegetation indices (VI) as proxies of the
solar-induced chlorophyll fluorescence (SIF) that will be captured
by FLEX. However, the highly dynamic nature of SIF demands a
very temporally accurate monitoring of S3 VIs to become reliable
proxies. In this scenario, this article proposes a novel temporal
reconstruction convolutional neural network (CNN), named dual
attention temporal CNN (DAT-CNN), which has been specially
designed for time-resolving S3 VIs using S2 and S3 multitemporal observations. In contrast to other existing techniques, DATCNN implements two different branches for processing and fusing
S2 and S3 multimodal data, while further exploiting intersensor
synergies. Besides, DAT-CNN also incorporates a new spatial–
spectral and temporal attention module to suppress uninformative spatial–spectral features, while focusing on the most relevant
temporal stamps for each particular prediction. The experimental
comparison, including several temporal reconstruction methods
and multiple operational Sentinel data products, demonstrates
the competitive advantages of the proposed model with respect to
the state of the art. The codes of this article will be available at
https://github.com/ibanezfd/DATCNN. [-]
Publicado en
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 15 (2022)Entidad financiadora
Ministerio de Ciencia, Innovación y Universidades
Identificador de la entidad financiadora
10.13039/100014440
Código del proyecto o subvención
RTI2018-098651-B-C54
Título del proyecto o subvención
Productos avanzados L3 y L4 para la misión FLEX-S· (FLEXL3L4)
Derechos de acceso
info:eu-repo/semantics/openAccess
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