Mostrar el registro sencillo del ítem
FloU-Net: An Optical Flow Network for Multi-modal Self-Supervised Image Registration
dc.contributor.author | Ibáñez Fernández, Damián | |
dc.contributor.author | Fernández Beltrán, Rubén | |
dc.contributor.author | Pla, Filiberto | |
dc.date.accessioned | 2023-05-18T18:53:38Z | |
dc.date.available | 2023-05-18T18:53:38Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | IBAÑEZ, Damian; FERNANDEZ-BELTRAN, Ruben; PLA, Filiberto. FloU-Net: An Optical Flow Network for Multimodal Self-Supervised Image Registration. IEEE Geoscience and Remote Sensing Letters, 2023, vol. 20, p. 1-5 | ca_CA |
dc.identifier.issn | 1545-598X | |
dc.identifier.issn | 1558-0571 | |
dc.identifier.uri | http://hdl.handle.net/10234/202545 | |
dc.description.abstract | Image registration is an essential task in image processing, where the final objective is to geometrically align two or more images. In remote sensing, this process allows comparing, fusing or analyzing data, specially when multi-modal images are used. In addition, multi-modal image registration becomes fairly challenging when the images have a significant difference in scale and resolution, together with local small image deformations. For this purpose, this paper presents a novel optical flow-based image registration network, named the FloU-Net, which tries to further exploit inter-sensor synergies by means of deep learning. The proposed method is able to extract spatial information from resolution differences and through an U-Net backbone generate an optical flow field estimation to accurately register small local deformations of multi-modal images in a self-supervised fashion. For instance, the registration between Sentinel-2 (S2) and Sentinel-3 (S3) optical data is not trivial, as there are considerable spectral-spatial differences among their sensors. In this case, the higher spatial resolution of S2 result in S2 data being a convenient reference to spatially improve S3 products, as well as those of the forthcoming Fluorescence Explorer (FLEX) mission, since image registration is the initial requirement to obtain higher data processing level products. To validate our method, we compare the proposed FloU-Net with other state-of-the-art techniques using 21 coupled S2/S3 optical images from different locations of interest across Europe. The comparison is performed through different performance measures. Results show that proposed FloU-Net can outperform the compared methods. The code and dataset are available in https://github.com/ibanezfd/FloU-Net. | ca_CA |
dc.format.extent | 6 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Institute of Electrical and Electronics Engineers | ca_CA |
dc.relation.isPartOf | IEEE Geoscience and Remote Sensing Letters, 2023, vol. 20, p. 1-5 | ca_CA |
dc.rights | “© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” | ca_CA |
dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | ca_CA |
dc.subject | Image Registration | ca_CA |
dc.subject | Convolutional Neural Networks | ca_CA |
dc.subject | Inter-sensor | ca_CA |
dc.subject | Multi-modal | ca_CA |
dc.subject | Multi-spectral | ca_CA |
dc.subject | Sentinel-2-3 | ca_CA |
dc.title | FloU-Net: An Optical Flow Network for Multi-modal Self-Supervised Image Registration | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | https://doi.org/10.1109/LGRS.2023.3249902 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://ieeexplore.ieee.org/abstract/document/10054383/keywords#keywords | ca_CA |
dc.type.version | info:eu-repo/semantics/acceptedVersion | ca_CA |
project.funder.name | Ministerio de Ciencia e Innovación | ca_CA |
project.funder.name | Generalitat Valenciana | ca_CA |
oaire.awardNumber | PID2021- 128794OB-I00 | ca_CA |
oaire.awardNumber | ACIF/2021/215 | ca_CA |
Ficheros en el ítem
Este ítem aparece en la(s) siguiente(s) colección(ones)
-
LSI_Articles [362]
Articles de publicacions periòdiques escrits per professors del Departament de Llenguatges i Sistemes Informàtics -
INIT_Articles [752]