Robust nonnegative matrix factorization for nonlinear unmixing of hyperspectral images

Nicolas Dobigeon (1, 2) , Cédric Févotte (3)

1 IRIT-SC - Signal et Communications
2 Toulouse INP - Institut National Polytechnique (Toulouse)
3 LAGRANGE - Joseph Louis LAGRANGE

This paper introduces a robust linear model to describe hyperspectral data arising from the mixture of several pure spectral signatures. This new model not only generalizes the commonly used linear mixing model but also allows for possible nonlinear effects to be handled, relying on mild assumptions regarding these nonlinearities. Based on this model, a nonlinear unmixing procedure is proposed. The standard nonnegativity and sum-to-one constraints inherent to spectral unmixing are coupled with a group-sparse constraint imposed on the nonlinearity component. The resulting objective function is minimized using a multiplicative algorithm. Simulation results obtained on synthetic and real data show that the proposed strategy competes with state-of-the-art linear and nonlinear unmixing methods.

Mots clés: Hyperspectral imagery; Nonlinear unmixing; Robustnonnegative matrix factorization; Group-sparsity

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]; Traitement du signal et de l'image [eess.SP]; Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]

Nicolas Dobigeon, Cédric Févotte. Robust nonnegative matrix factorization for nonlinear unmixing of hyperspectral images. IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing - WHISPERS 2013, Jun 2013, Gainesville, United States. pp. 1-4. ⟨hal-01151017⟩