Correntropy Maximization via ADMM: Application to Robust Hyperspectral Unmixing

Fei Zhu, Abderrahim Halimi, Paul Honeine, Badong Chen, Nanning Zheng

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)
70 Downloads (Pure)


In hyperspectral images, some spectral bands suffer from low signal-to-noise ratio due to noisy acquisition and atmospheric effects, thus requiring robust techniques for the unmixing problem. This paper presents a robust supervised spectral unmixing approach for hyperspectral images. The robustness is
achieved by writing the unmixing problem as the maximization of the correntropy criterion subject to the most commonly used constraints. Two unmixing problems are derived: the first problem considers the fully-constrained unmixing, with both the non-negativity and sum-to-one constraints, while the second one deals with the non-negativity and the sparsity-promoting of the abundances. The corresponding optimization problems are solved using an alternating direction method of multipliers (ADMM) approach. Experiments on synthetic and real hyperspectral images validate the performance of the proposed algorithms for different scenarios, demonstrating that the correntropy-based unmixing with ADMM is particularly robust against highly noisy outlier bands.
Original languageEnglish
Article number7964753
Pages (from-to)4944-4955
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number9
Early online date30 Jun 2017
Publication statusPublished - Sept 2017


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