TRANSFERING STATISTICAL METHODS TO REMOTE SENSING: A PLATFORM TO PROVIDE SATELLITE IMAGES WITH IMPROVED SPATIO-TEMPORAL RESOLUTIONS
PDC2021-120796-I00
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Nombre agencia financiadora Agencia Estatal de Investigación
Acrónimo agencia financiadora AEI
Programa Programa Estatal de I+D+i orientada a los Retos de la Sociedad
Subprograma Subprograma Estatal de Transferencia de Conocimiento
Convocatoria Proyectos I+D+i Pruebas de Concepto
Año convocatoria 2021
Unidad de gestión Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020
Centro beneficiario UNIVERSIDAD PUBLICA DE NAVARRA
Identificador persistente http://dx.doi.org/10.13039/501100011033
Publicaciones
Resultados totales (Incluyendo duplicados): 1
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Unpaired spatio-temporal fusion of image patches (USTFIP) from cloud covered images
Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
- Goyena Baroja, Harkaitz
- Pérez Goya, Unai
- Montesino San Martín, Manuel
- Militino, Ana F.
- Wang, Qunming
- Atkinson, Peter M.
- Ugarte Martínez, María Dolores
Spatio-temporal image fusion aims to increase the frequency and resolution of multispectral satellite sensor images in a cost-effective manner. However, practical constraints on input data requirements and computational cost prevent a wider adoption of these methods in real case-studies. We propose an ensemble of strategies to eliminate the need for cloud-free matching pairs of satellite sensor images. The new methodology called Unpaired Spatio-Temporal Fusion of Image Patches (USTFIP) is tested in situations where classical requirements are progressively difficult to meet. Overall, the study shows that USTFIP reduces the root mean square error by 2-to-13% relative to the state-of-the-art Fit-FC fusion method, due to an efficient use of the available information. Implementation of USTFIP through parallel computing saves up to 40% of the computational time required for Fit-FC., This research was supported by the Spanish Research Agency and
Next Generation EU (PDC2021-120796-I00 project) and by the Spanish
Research Agency (PID 2020-113125RB-I00/MCIN/AEI/10.13039/
501100011033 project).
This work was supported by the National Natural Science Foundation
of China under Grant 42222108.
Next Generation EU (PDC2021-120796-I00 project) and by the Spanish
Research Agency (PID 2020-113125RB-I00/MCIN/AEI/10.13039/
501100011033 project).
This work was supported by the National Natural Science Foundation
of China under Grant 42222108.