This communication proposes an unsupervised neighbor dependent nonlinear unmixing algorithm for hyperspectral data. The proposed mixing scheme models the reflectance vector of a pixel as the sum of a linear combination of the endmem-bers plus a nonlinear function acting on neighboring spectra. The nonlinear function belongs to a reproducing kernel Hilbert space. The observations themselves are considered as the endmember candidates, and the group lasso regulariza-tion is used to enable selecting the purest pixels among the candidates. Experiments on synthetic data demonstrate the effectiveness of the proposed approach.
['Rita Ammanouil', 'André Ferrari', 'Cedric Richard']
Unsupervised neighbor dependent nonlinear unmixing