Result: Tensor decomposition based multisensor hyperspectral fusion for spatial super resolution

Title:
Tensor decomposition based multisensor hyperspectral fusion for spatial super resolution
Authors:
M'Rani, Fatima Zahrae, Puigt, Matthieu, Jamet, Cédric, Delmaire, Gilles, Guilloteau, Claire, Roussel, Gilles, Vantrepotte, Vincent
Contributors:
Laboratoire d'Informatique Signal et Image de la Côte d'Opale (LISIC), Université du Littoral Côte d'Opale (ULCO), Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 (LOG), Institut national des sciences de l'Univers (INSU - CNRS)-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [Ile-de-France]), Région Hauts-de-France, GRSS (Geoscience and Remote Sensing Society), IEEE (Institute of Electrical and Electronics Engineers), ANR-21-EXES-0011,IFSEA,Transdisciplinary graduate school for marIne, Fisheries and SEAfood sciences(2021)
Source:
15th Workshop on Hyperspectral Image and Signal Processing: Evolutions in Remote Sensing (IEEE WHISPERS 2025), GRSS (Geoscience and Remote Sensing Society); IEEE (Institute of Electrical and Electronics Engineers), Nov 2025, Bellaterra (Barcelona), Spain
Publisher Information:
CCSD, 2025.
Publication Year:
2025
Collection:
collection:IRD
collection:INSU
collection:CNRS
collection:UNIV-LITTORAL
collection:UNIV-LILLE
collection:ANR
collection:LISIC
collection:LOG
collection:ANR-OCEANS-19TO21
collection:ANR-OCEANS
Subject Terms:
Generalized Sylvester equation, Tensor decomposition, Multi-sharpening, [INFO.INFO-TI]Computer Science [cs], Image Processing [eess.IV], [SPI.SIGNAL]Engineering Sciences [physics], Signal and Image processing
Subject Geographic:
Bellaterra (Barcelona), Spain
Original Identifier:
HAL: hal-05328866
Document Type:
Conference conferenceObject<br />Conference papers
Language:
English
Access URL:
https://hal.science/hal-05328866
https://hal.science/hal-05328866v1/document
https://hal.science/hal-05328866v1/file/fzmmpcjgdcggrvv_WHISPERS2025.pdf
Rights:
info:eu-repo/semantics/OpenAccess
URL: http://creativecommons.org/licenses/by/
Accession Number:
edshal.hal.05328866v1
Database:
HAL

Further Information

In this paper, we address the multi-sharpening problem by simultaneously fusing multiple multispectral images with a single hyperspectral image. We propose two variants of a novel strategy named G-STEREO-1 and G-STEREO-2. Both methods extend the tensor-based multi-sharpening framework named STEREO, by leveraging the complementarity of several multispectral sources to enhance fusion quality. G-STEREO-1 and G-STEREO-2 are based on a joint tensor decomposition model that incorporates a generalized Sylvester equation within a Canonical Polyadic (CP) tensor decomposition scheme. Our approaches overcome the limitations of existing joint tensor-based fusion techniques, which are restricted to fusing only a single multispectral image with a hyperspectral one. Experimental results show that both G-STEREO-1 and G-STEREO-2 consistently outperform these existing methods.