Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Ricardo A. Borsoi"'
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 61:1-15
Hyperspectral and multispectral image fusion allows us to overcome the hardware limitations of hyperspectral imaging systems inherent to their lower spatial resolution. Nevertheless, existing algorithms usually fail to consider realistic image acquis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b37125dcd07d32056d4825aae5e981eb
https://doi.org/10.1109/tgrs.2023.3273118
https://doi.org/10.1109/tgrs.2023.3273118
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::002f771de4c9d31c1eb0706884e38a12
https://doi.org/10.1109/icassp49357.2023.10095764
https://doi.org/10.1109/icassp49357.2023.10095764
Publikováno v:
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::323ed816c0b03521bb7cd1dcc21418fc
https://doi.org/10.1109/icassp49357.2023.10095321
https://doi.org/10.1109/icassp49357.2023.10095321
Multitemporal hyperspectral unmixing (MTHU) is a fundamental tool in the analysis of hyperspectral image sequences. It reveals the dynamical evolution of the materials (endmembers) and of their proportions (abundances) in a given scene. However, adeq
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e30b50de04280ccfbf0d21c0d901605c
http://arxiv.org/abs/2303.10566
http://arxiv.org/abs/2303.10566
Publikováno v:
2022 56th Asilomar Conference on Signals, Systems, and Computers.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6342f5c6e3c3ae2e9f04245dfb314425
https://doi.org/10.1109/ieeeconf56349.2022.10051954
https://doi.org/10.1109/ieeeconf56349.2022.10051954
Autor:
David Brie, Ricardo Augusto Borsoi, Jose C. M. Bermudez, Cedric Richard, Clémence Prévost, Konstantin Usevich
Publikováno v:
IEEE Journal of Selected Topics in Signal Processing
IEEE Journal of Selected Topics in Signal Processing, IEEE, 2021, 15 (3), pp.702-717. ⟨10.1109/JSTSP.2021.3054338⟩
IEEE Journal of Selected Topics in Signal Processing, IEEE, 2021, 15 (3), pp.702-717. ⟨10.1109/JSTSP.2021.3054338⟩
International audience; Coupled tensor approximation has recently emerged as a promising approach for the fusion of hyperspectral and multispectral images, reconciling state of the art performance with strong theoretical guarantees. However, tensor-b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2de5b209f08173ee61227ed8a439f9e5
https://doi.org/10.1109/jstsp.2021.3054338
https://doi.org/10.1109/jstsp.2021.3054338
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 58:1833-1842
Traditional hyperspectral unmixing methods neglect the underlying variability of spectral signatures often observed in typical hyperspectral images (HI), propagating these missmodeling errors throughout the whole unmixing process. Attempts to model m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a427c01b2ed8275df152eaee2e253b3
https://doi.org/10.1109/tgrs.2019.2949543
https://doi.org/10.1109/tgrs.2019.2949543
Publikováno v:
IEEE Transactions on Image Processing. 29:3638-3651
Spectral variability in hyperspectral images can result from factors including environmental, illumination, atmospheric and temporal changes. Its occurrence may lead to the propagation of significant estimation errors in the unmixing process. To addr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3e0b3e2ea5e2d02775e5458841cbc8e
https://doi.org/10.1109/tip.2020.2963959
https://doi.org/10.1109/tip.2020.2963959
Publikováno v:
IEEE Transactions on Computational Imaging. 6:374-384
Endmember (EM) spectral variability can greatly impact the performance of standard hyperspectral image analysis algorithms. Extended parametric models have been successfully applied to account for the EM spectral variability. However, these models st
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f4b6160552d7c0b221a6fb2cb8d0245
https://doi.org/10.1109/tci.2019.2948726
https://doi.org/10.1109/tci.2019.2948726
Publikováno v:
IEEE Geoscience and Remote Sensing Letters
IEEE Geoscience and Remote Sensing Letters, IEEE-Institute of Electrical and Electronics Engineers, 2022, 19, pp.1-5. ⟨10.1109/LGRS.2020.3025781⟩
IEEE Geoscience and Remote Sensing Letters, IEEE-Institute of Electrical and Electronics Engineers, 2022, 19, pp.1-5. ⟨10.1109/LGRS.2020.3025781⟩
The recent evolution of hyperspectral imaging technology and the proliferation of new emerging applications presses for the processing of multiple temporal hyperspectral images. In this work, we propose a novel spectral unmixing (SU) strategy using p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d6d8b5805fbfdca5d30794aa8dc11e5
https://hal.archives-ouvertes.fr/hal-03505744
https://hal.archives-ouvertes.fr/hal-03505744