Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing
Résumé
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 nonnegativity and sum-to-one constraints, while the second one deals with the nonnegativity 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.
Mots clés
machine learning
hyperspectral
geophysical image processing
hyperspectral imaging
learning (artificial intelligence)
maximum entropy methods
remote sensing
correntropy maximization
ADMM
alternating direction method of multipliers
hyperspectral images
robust supervised spectral unmixing
fully constrained unmixing
cuprite mining image
optimization
robustness
kernel
noise measurement
convex functions
alternating direction method of multipliers (ADMM)
correntropy
hyperspectral image
maximum correntropy estimation
unmixing problem
| Origine | Publication financée par une institution |
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