W-NetPan: Double-U network for inter-sensor self-supervised pan-sharpening
Impacto
Scholar |
Otros documentos de la autoría: Fernandez-Beltran, Ruben; Fernandez-Botran, Rafael; kang, jian; Pla, Filiberto
Metadatos
Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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INVESTIGACIONMetadatos
Título
W-NetPan: Double-U network for inter-sensor self-supervised pan-sharpeningFecha de publicación
2023-02-08Editor
Elsevier ScienceDirectISSN
0925-2312; 1872-8286Cita bibliográfica
Fernandez-Beltran, R., Fernandez, R., Kang, J., & Pla, F. (2023). W-NetPan: Double-U network for inter-sensor self-supervised pan-sharpening. Neurocomputing, 530, 125-138.Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
The increasing availability of remote sensing data allows dealing with spatial-spectral limitations by means of pan-sharpening methods. However, fusing inter-sensor data poses important challenges, in terms of resolution ... [+]
The increasing availability of remote sensing data allows dealing with spatial-spectral limitations by means of pan-sharpening methods. However, fusing inter-sensor data poses important challenges, in terms of resolution differences, sensor-dependent deformations and ground-truth data availability, that demand more accurate pan-sharpening solutions. In response, this paper proposes a novel deep learning-based pan-sharpening model which is termed as the double-U network for self-supervised pan-sharpening (W-NetPan). In more details, the proposed architecture adopts an innovative W-shape that integrates two U-Net segments which sequentially work for spatially matching and fusing inter-sensor multi-modal data. In this way, a synergic effect is produced where the first segment resolves inter-sensor deviations while stimulating the second one to achieve a more accurate data fusion. Additionally, a joint loss formulation is proposed for effectively training the proposed model without external data supervision. The experimental comparison, conducted over four coupled Sentinel-2 and Sentinel-3 datasets, reveals the advantages of W-NetPan with respect to several of the most important state-of-the-art pan-sharpening methods available in the literature. The codes related to this paper will be available at https://github.com/rufernan/WNetPan. [-]
Publicado en
Neurocomputing, Vol. 530 (April 2023)Entidad financiadora
Ministerio de Ciencia e Innovación (Spain) | National Natural Science Foundation of China
Código del proyecto o subvención
PID2021-128794OB-I00 | Grant 62101371
Derechos de acceso
info:eu-repo/semantics/openAccess
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