Zobrazeno 1 - 10
of 221
pro vyhledávání: '"Yannick Deville"'
Autor:
Alain Deville, Yannick Deville
Publikováno v:
Information, Vol 15, Iss 5, p 247 (2024)
Quantum information mobilizes the description of quantum systems, their states, and their behavior. Since a measurement postulate introduced by von Neumann in 1932, if a quantum system has been prepared in two different mixed states represented by th
Externí odkaz:
https://doaj.org/article/424aae11381f430fb632a96e33b0b6ed
Publikováno v:
IEEE Access, Vol 11, Pp 100632-100645 (2023)
This paper presents a novel Blind Source Separation method that can handle convolutive mixtures that may be underdetermined. Our method combines TF masking and beamforming and exploits the source signals sparsity in the Time-Frequency (TF) domain. Re
Externí odkaz:
https://doaj.org/article/5780af618f8943babba9cb4d2ba4fb9c
Autor:
Yannick Deville, Salah-Eddine Brezini, Fatima Zohra Benhalouche, Moussa Sofiane Karoui, Mireille Guillaume, Xavier Lenot, Bruno Lafrance, Malik Chami, Sylvain Jay, Audrey Minghelli, Xavier Briottet, Véronique Serfaty
Publikováno v:
Remote Sensing, Vol 15, Iss 18, p 4583 (2023)
In a previous paper, we introduced (i) a specific hyperspectral mixing model for the sea bottom, based on a detailed physical analysis that includes the adjacency effect, and (ii) an associated unmixing method that is supervised (i.e., not blind) in
Externí odkaz:
https://doaj.org/article/075365cca8fa4f398918dd48b9b2bd27
Autor:
Yannick Deville, Alain Deville
Publikováno v:
IEEE Transactions on Quantum Engineering, Vol 2, Pp 1-24 (2021)
The term “machine learning” especially refers to algorithms that derive mappings, i.e., input–output transforms, by using numerical data that provide information about considered transforms. These transforms appear in many problems related to c
Externí odkaz:
https://doaj.org/article/beacdeda604c4b019a79b2f292601739
Autor:
Salah Eddine Brezini, Yannick Deville
Publikováno v:
Sensors, Vol 23, Iss 4, p 2341 (2023)
The aim of fusing hyperspectral and multispectral images is to overcome the limitation of remote sensing hyperspectral sensors by improving their spatial resolutions. This process, also known as hypersharpening, generates an unobserved high-spatial-r
Externí odkaz:
https://doaj.org/article/947b20698e6641bba3d17753e1a8c5a1
Autor:
Yohann Constans, Sophie Fabre, Michael Seymour, Vincent Crombez, Yannick Deville, Xavier Briottet
Publikováno v:
Remote Sensing, Vol 14, Iss 1, p 113 (2021)
Hyperspectral pansharpening methods in the reflective domain are limited by the large difference between the visible panchromatic (PAN) and hyperspectral (HS) spectral ranges, which notably leads to poor representation of the SWIR (1.0–2.5 μm) spe
Externí odkaz:
https://doaj.org/article/0e33872bfe7f48c8b8c3dd46c5152bd5
Publikováno v:
Remote Sensing, Vol 13, Iss 11, p 2132 (2021)
Unsupervised hyperspectral unmixing methods aim to extract endmember spectra and infer the proportion of each of these spectra in each observed pixel when considering linear mixtures. However, the interaction between sunlight and the Earth’s surfac
Externí odkaz:
https://doaj.org/article/023356e21a114e96bb8526b74c4dc9a0
Publikováno v:
Remote Sensing, Vol 12, Iss 17, p 2834 (2020)
Hyperspectral unmixing is a widely studied field of research aiming at estimating the pure material signatures and their abundance fractions from hyperspectral images. Most spectral unmixing methods are based on prior knowledge and assumptions that i
Externí odkaz:
https://doaj.org/article/729a861da14f4a39a3b91886d44e1b15
Publikováno v:
Remote Sensing, Vol 12, Iss 19, p 3198 (2020)
Blind source separation (or unmixing) methods process a set of mixed signals, which are typically linear memoryless combinations of source signals, so as to estimate these unknown source signals and/or combination coefficients. These methods have bee
Externí odkaz:
https://doaj.org/article/4c4622d89e9e4088871872839210d864
Autor:
Mireille Guillaume, Audrey Minghelli, Yannick Deville, Malik Chami, Louis Juste, Xavier Lenot, Bruno Lafrance, Sylvain Jay, Xavier Briottet, Veronique Serfaty
Publikováno v:
Remote Sensing, Vol 12, Iss 13, p 2072 (2020)
Monitoring of coastal areas by remote sensing is an important issue. The interest of using an unmixing method to determine the seabed composition from hyperspectral aerial images of coastal areas is investigated. Unmixing provides both seabed abundan
Externí odkaz:
https://doaj.org/article/e810bc85a4df49f3a3ec295def853b3b