Remote Sensing Single-Image Superresolution Based on a Deep Compendium Model
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Otros documentos de la autoría: Haut, Juan M.; Paoletti, Mercedes Eugenia; Fernandez-Beltran, Ruben; Plaza, Javier; Plaza, Antonio; Li, Jun
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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INVESTIGACIONMetadatos
Título
Remote Sensing Single-Image Superresolution Based on a Deep Compendium ModelAutoría
Fecha de publicación
2019-03Editor
IEEECita bibliográfica
HAUT, J. M., et al. Remote Sensing Single-Image Superresolution Based on a Deep Compendium Model. IEEE Geoscience and Remote Sensing Letters, 2019.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://ieeexplore.ieee.org/abstract/document/8660433/metrics#metricsVersión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
This letter introduces a novel remote sensing single-image superresolution (SR) architecture based on a deep efficient compendium model. The current deep learning-based SR trend stands for using deeper networks to ... [+]
This letter introduces a novel remote sensing single-image superresolution (SR) architecture based on a deep efficient compendium model. The current deep learning-based SR trend stands for using deeper networks to improve the performance. However, this practice often results in the degradation of visual results. To address this issue, the proposed approach harmonizes several different improvements on the network design to achieve state-of-the-art performance when superresolving remote sensing imagery. On the one hand, the proposal combines residual units and skip connections to extract more informative features on both local and global image areas. On the other hand, it makes use of parallelized 1x1 convolutional filters (network in network) to reconstruct the superresolved result while reducing the information loss through the network. Our experiments, conducted using seven different SR methods over the well-known UC Merced remote sensing data set, and two additional GaoFen-2 test images, show that the proposed model is able to provide competitive advantages. [-]
Proyecto de investigación
Spanish Ministry (Grant FPU14/02012-FPU15/020909 ; Generalitat Valenciana (Grant APOSTD/2017/007) ; Junta de Extremadura (Grant GR15005) ; Ministerio de Economía y Empresa, Spain (Project TIN2015-63646-C5-5-R).Derechos de acceso
© 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
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info:eu-repo/semantics/openAccess
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/openAccess
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