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

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

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 the sense that it requires a prior estimation of v...

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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: MDPI AG 2023-09-01
Series:Remote Sensing
Subjects:
hyperspectral unsupervised unmixing
hyperspectral blind unmixing
spectral variability
intraclass variability
sea bottom unmixing
adjacency effect
Online Access:https://www.mdpi.com/2072-4292/15/18/4583

Internet

https://www.mdpi.com/2072-4292/15/18/4583

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