Nonlinear Hyperspectral Unmixing With Robust Nonnegative Matrix Factorization - Archive ouverte HAL Access content directly
Journal Articles IEEE Transactions on Image Processing Year : 2015

Nonlinear Hyperspectral Unmixing With Robust Nonnegative Matrix Factorization

Abstract

We introduce a robust mixing model to describe hyperspectral data resulting from the mixture of several pure spectral signatures. The new model extends the commonly used linear mixing model by introducing an additional term accounting for possible nonlinear effects, that are treated as sparsely distributed additive outliers.With the standard nonnegativity and sum-to-one constraints inherent to spectral unmixing, our model leads to a new form of robust nonnegative matrix factorization with a group-sparse outlier term. The factorization is posed as an optimization problem which is addressed with a block-coordinate descent algorithm involving majorization-minimization updates. Simulation results obtained on synthetic and real data show that the proposed strategy competes with state-of-the-art linear and nonlinear unmixing methods.
Fichier principal
Vignette du fichier
Fevotte_14289.pdf (280.56 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01212736 , version 1 (07-10-2015)

Identifiers

Cite

Cédric Févotte, Nicolas Dobigeon. Nonlinear Hyperspectral Unmixing With Robust Nonnegative Matrix Factorization. IEEE Transactions on Image Processing, 2015, vol. 24 (n° 12), pp. 4810-4819. ⟨10.1109/TIP.2015.2468177⟩. ⟨hal-01212736⟩
171 View
178 Download

Altmetric

Share

Gmail Facebook X LinkedIn More