Remote Sensing Single-Image Superresolution Based on a Deep Compendium Model
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Other documents of the author: Haut, Juan M.; Paoletti, Mercedes Eugenia; Fernandez-Beltran, Ruben; Plaza, Javier; Plaza, Antonio; Li, Jun
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comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
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Title
Remote Sensing Single-Image Superresolution Based on a Deep Compendium ModelAuthor (s)
Date
2019-03Publisher
IEEEBibliographic citation
HAUT, J. M., et al. Remote Sensing Single-Image Superresolution Based on a Deep Compendium Model. IEEE Geoscience and Remote Sensing Letters, 2019.Type
info:eu-repo/semantics/articlePublisher version
https://ieeexplore.ieee.org/abstract/document/8660433/metrics#metricsVersion
info:eu-repo/semantics/submittedVersionSubject
Abstract
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. [-]
Investigation project
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).Rights
© 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
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