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Matrix Cofactorization for Joint Unmixing and Classification of Hyperspectral Images

Abstract : This paper introduces a matrix cofactorization approach to perform spectral unmixing and classification jointly. After formulating the unmixing and classification tasks as matrix factorization problems, a link is introduced between the two coding matrices, namely the abundance matrix and the feature matrix. This coupling term can be interpreted as a clustering term where the abundance vectors are clustered and the resulting attribution vectors are then used as feature vectors. The overall non-smooth, non-convex optimization problem is solved using a proximal alternating linearized minimization algorithm (PALM) ensuring convergence to a critical point. The quality of the obtained results is finally assessed by comparison to other conventional algorithms on semi-synthetic yet realistic dataset.
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Submitted on : Thursday, January 16, 2020 - 11:38:42 AM
Last modification on : Monday, April 5, 2021 - 2:26:06 PM
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  • HAL Id : hal-02442017, version 1
  • OATAO : 24982


Adrien Lagrange, Mathieu Fauvel, Stéphane May, José M. Bioucas-Dias, Nicolas Dobigeon. Matrix Cofactorization for Joint Unmixing and Classification of Hyperspectral Images. 27th European Signal Processing Conference (EUSIPCO 2019), Sep 2019, A Coruna, Spain. pp.1-5. ⟨hal-02442017⟩



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