Modeling and Unsupervised Unmixing Based on Spectral Variability for Hyperspectral Oceanic Remote Sensing Data with Adjacency Effects

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

Hyperspectral and Multispectral Image Fusion with Automated Extraction of Image-Based Endmember Bundles and Sparsity-Based Unmixing to Deal with Spectral Variability

Autor: Salah Eddine Brezini, Yannick Deville

Publikováno v: Sensors
Volume 23
Issue 4
Pages: 2341

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://explore.openaire.eu/search/publication?articleId=doi_dedup___::09c8f3993a0f8c99d756611d658e3065
https://doi.org/10.3390/s23042341

A Penalization-Based NMF Approach for Hyperspectral Unmixing Addressing Spectral Variability with an Additively-Tuned Mixing Model

Autor: Yannick Deville, Abdelaziz Ouamri, Moussa Sofiane Karoui, Fatima Zohra Benhalouche, Salah Eddine Brezini

Publikováno v: IGARSS

Remote sensing hyperspectral sensors are often limited in their spatial resolutions, which leads to mixed pixels. The linear spectral unmixing process is frequently used to extract endmember spectra and their abundance fractions. The standard linear

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c64917b26f576d72320bce34a0fd41b9
https://doi.org/10.1109/igarss47720.2021.9553366

Hyperspectral Oceanic Remote Sensing With Adjacency Effects: From Spectral-Variability-Based Modeling To Performance Of Associated Blind Unmixing Methods

Autor: Xavier Briottet, Bruno Lafrance, Yannick Deville, Salah Eddine Brezini, Fatima Zohra Benhalouche, Sylvain Jay, X. Lenot, Mireille Guillaume, V. Serfaty, Malik Chami, Audrey Minghelli, Moussa Sofiane Karoui

Publikováno v: IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium
IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Jul 2019, Yokohama, Japan. pp.282-285, ⟨10.1109/IGARSS.2019.8898430⟩
IGARSS

International audience; In a very recent paper, we introduced (i) a specific hyper-spectral mixing model for the sea bottom, based on a detailed physical analysis which includes the adjacency effect, and (ii) an associated unmixing method, which is n

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::133f0fee5e99550dc881b69612a22105
https://hal-insu.archives-ouvertes.fr/insu-02389212