Joint unmixing-deconvolution algorithms for hyperspectral images

Yingying Song (1) , El-Hadi Djermoune (1) , David Brie (1) , Cédric Richard (2)

1 CRAN - Centre de Recherche en Automatique de Nancy
2 LAGRANGE - Joseph Louis LAGRANGE

This paper combines supervised linear unmixing and deconvolution problems to increase the resolution of the abundance maps for industrial imaging systems. The joint unmixing-deconvolution (JUD) algorithm is introduced based on the Tikhonov regularization criterion for offline processing. In order to meet the needs of industrial applications, the proposed JUD algorithm is then extended for online processing by using a block Tikhonov criterion. The performance of JUD is increased by adding a non-negativity constraint which is implemented in a fast way using the quadratic penalty method and fast Fourier transform. The proposed algorithm is then assessed using both simulated and real hyperspectral images.

Mots clés: Hyperspectral image unmixing; non-negative Tikhonov regularization; hyperspectral image deconvolution

Domaines: Traitement du signal et de l'image [eess.SP]

Yingying Song, El-Hadi Djermoune, David Brie, Cédric Richard. Joint unmixing-deconvolution algorithms for hyperspectral images. 27th European Signal Processing Conference, EUSIPCO 2019, Sep 2019, Coruna, Spain. ⟨hal-02305958⟩