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Matrix cofactorization for joint spatial-spectral unmixing of hyperspectral images

Lagrange, Adrien and Fauvel, Mathieu and May, Stéphane and Dobigeon, Nicolas Matrix cofactorization for joint spatial-spectral unmixing of hyperspectral images. (2020) IEEE Transactions on Geoscience and Remote Sensing, 58 (7). 4915-4927. ISSN 0196-2892

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Official URL: https://doi.org/10.1109/TGRS.2020.2968541

Abstract

Hyperspectral unmixing aims at identifying a set of elementary spectra and the corresponding mixture coefficients for each pixel of an image. As the elementary spectra correspond to the reflectance spectra of real materials, they are often very correlated, thus yielding an ill-conditioned problem. To enrich the model and reduce ambiguity due to the high correlation, it is common to introduce spatial information to complement the spectral information. The most common way to introduce spatial information is to rely on a spatial regularization of the abundance maps. In this article, instead of considering a simple but limited regularization process, spatial information is directly incorporated through the newly proposed context of spatial unmixing. Contextual features are extracted for each pixel, and this additional set of observations is decomposed according to a linear model. Finally, the spatial and spectral observations are unmixed jointly through a cofactorization model. In particular, this model introduces a coupling term used to identify clusters of shared spatial and spectral signatures. An evaluation of the proposed method is conducted on synthetic and real data and shows that results are accurate and also very meaningful since they describe both spatially and spectrally the various areas of the scene.

Item Type:Article
Additional Information:Thanks to IEEE editor. The definitive version is available at http://ieeexplore.ieee.org The original PDF can be found at IEEE Transactions on Geoscience and Remote Sensing website : https://ieeexplore.ieee.org/document/8978724 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National d'Études Spatiales - CNES (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Laboratory name:
Funders:
ANR : Agence Nationale de la Recherche (France) - CNES : Centre National d’Études Spatiales (France) - Région Occitanie (France) - Union européenne (Europe) - ANITI : Artificial and Natural Intelligence Toulouse Institute (France)
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Deposited On:28 Aug 2020 09:16

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