A Modified Non-Negative LMS Algorithm and its Stochastic Behavior Analysis

Jie Chen (1, 2) , Cédric Richard (2) , José C. M. Bermudez (3) , Paul Honeine (1)

1 LM2S - Laboratoire Modélisation et Sûreté des Systèmes
2 FIZEAU - Laboratoire Hippolyte Fizeau
3 UFSC - Universidade Federal de Santa Catarina = Federal University of Santa Catarina [Florianópolis]

In hyperspectral images, pixels are mixtures of spectral components associated to pure materials. Although the linear mixture model is the most studied case, nonlinear models have been taken into consideration to overcome some limitations of the linear model. In this paper, nonlinear hyperspectral unmixing problem is studied through kernel-based learning theory. Endmember components at each band are mapped implicitly in a high feature space, in order to address the nonlinear interaction of photons. Experiment results with both synthetic and real images illustrate the effectiveness of the proposed scheme.

Mots clés: non-negativity; adaptive filtering; feature extraction; learning (artificial intelligence); least mean squares methods; modified nonnegative LMS algorithm; stochastic behavior analysis; hyperspectral image; spectral components; nonlinear hyperspectral unmixing problem; Mathematical model; Equations; kernel-based learning theory; end member components; feature space; nonlinear interaction; synthetic images; real images; Convergence; Signal processing algorithms; Vectors; Stochastic processes; Approximation methods

Domaines: Apprentissage [cs.LG]; Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Jie Chen, Cédric Richard, José C. M. Bermudez, Paul Honeine. A Modified Non-Negative LMS Algorithm and its Stochastic Behavior Analysis. Proc. 45th Asilomar Conference on Signals, Systems and Computers (ASILOMAR), 2011, Pacific Grove (CA), USA, United States. pp.542-546, ⟨10.1109/ACSSC.2011.6190060⟩. ⟨hal-01966036⟩