Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 24, Issue 12, Pages 4810-4819Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2015.2468177
Keywords
Hyperspectral imagery; nonlinear unmixing; robust nonnegative matrix factorization; group-sparsity
Funding
- Centre National de la Recherche Scientifique through PEPS Project ESTOMAT
- Agence Nationale de la Recherche Hypanema [ANR-12-BS03-003]
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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 the state-of-the-art linear and nonlinear unmixing methods.
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