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

Abstract : 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 not blind in the sense that it requires a prior estimation of various parameters of that mixing model. We here proceed much further, by first analytically showing that this model can be seen as a specific member of the general class of mixing models involving spectral variability. Therefore, we then process such data with the IP-NMF and UP-NMF blind unmixing methods that we recently proposed in other works to handle spectral variability. Such a variability especially occurs when sea depth significantly varies over the considered scene, and we show that IP-NMF and UP-NMF then yield significantly better pure spectra estimation than a classical method from the literature which was not designed to handle such a variability.
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https://hal-insu.archives-ouvertes.fr/insu-02389212
Contributor : Catherine Cardon <>
Submitted on : Monday, December 2, 2019 - 2:02:27 PM
Last modification on : Tuesday, August 18, 2020 - 11:18:02 AM

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Yannick Deville, Salah-Eddine Brezini, Fatima Benhalouche, Moussa Sofiane Karoui, Mireille Guillaume, et al.. Hyperspectral Oceanic Remote Sensing With Adjacency Effects: From Spectral-Variability-Based Modeling To Performance Of Associated Blind Unmixing Methods. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Jul 2019, Yokohama, Japan. pp.282-285, ⟨10.1109/IGARSS.2019.8898430⟩. ⟨insu-02389212⟩

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