Remote Sensing Image Superresolution Using Deep Residual Channel Attention
View/ Open
Impact
Scholar |
Other documents of the author: Haut, Juan M.; Fernandez-Beltran, Ruben; Paoletti, Mercedes Eugenia; Plaza, Javier; Plaza, Antonio
Metadata
Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/43662
comunitat-uji-handle3:10234/43643
comunitat-uji-handle4:
INVESTIGACIONMetadata
Title
Remote Sensing Image Superresolution Using Deep Residual Channel AttentionAuthor (s)
Date
2019-07-23Publisher
IEEEBibliographic citation
HAUT, Juan Mario, et al. Remote Sensing Image Superresolution Using Deep Residual Channel Attention. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57.11: 9277-9289Type
info:eu-repo/semantics/articlePublisher version
https://ieeexplore.ieee.org/abstract/document/8770258Version
info:eu-repo/semantics/submittedVersionSubject
Abstract
The current trend in remote sensing image superresolution (SR) is to use supervised deep learning models to effectively enhance the spatial resolution of airborne and satellite-based optical imagery. Nonetheless, the ... [+]
The current trend in remote sensing image superresolution (SR) is to use supervised deep learning models to effectively enhance the spatial resolution of airborne and satellite-based optical imagery. Nonetheless, the inherent complexity of these architectures/data often makes these methods very difficult to train. Despite these recent advances, the huge amount of network parameters that must be fine-tuned and the lack of suitable high-resolution remotely sensed imagery in actual operational scenarios still raise some important challenges that may become relevant limitations in the existent earth observation data production environments. To address these problems, we propose a new remote sensing SR approach that integrates a visual attention mechanism within a residual-based network design in order to allow the SR process to focus on those features extracted from land-cover components that require more computations to be superresolved. As a result, the network training process is significantly improved because it aims at learning the most relevant high-frequency information while the proposed architecture allows neglecting the low-frequency features extracted from spatially uninformative earth surface areas by means of several levels of skip connections. Our experimental assessment, conducted using the University of California at Merced and GaoFen-2 remote sensing image collections, three scaling factors, and eight different SR methods, demonstrates that our newly proposed approach exhibits competitive performance in the task of superresolving remotely sensed imagery. [-]
Investigation project
Spanish Ministry (Grant FPU14/02012-FPU15/02090) ; Junta de Extremadura (Grant GR15005) ; Generalitat Valenciana (Grant APOSTD/2017/007) ; MINECO (Grant TIN2015-63646-C5-5-R) ; European Union (Grant 734541-EOXPOSURE)Rights
© 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/openAccess
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/openAccess
This item appears in the folowing collection(s)
- INIT_Articles [754]