ADMM for Maximum Correntropy Criterion
Résumé
The correntropy provides a robust criterion for outlier-insensitive machine learning, and its maximisation has been increasingly investigated in signal and image processing. In this paper, we investigate the problem of unmixing hyperspectral images, namely decomposing each pixel/spectrum of a given image as a linear combination of other pixels/spectra called endmembers. The coefficients of the combination need to be estimated subject to the nonnegativity and the sum-to-one constraints. In practice, some spectral bands suffer from low signal-to-noise ratio due to acquisition noise and atmospheric effects, thus requiring robust techniques for the unmixing problem. In this work, we cast the unmixing problem as the maximization of a correntropy criterion, and provide a relevant solution using the alternating direction method of multipliers (ADMM) method. Finally, the relevance of the proposed approach is validated on synthetic and real hyperspectral images, demonstrating that the correntropy-based unmixing is robust to outlier bands.
Mots clés
feature extraction
image denoising
image reconstruction
learning (artificial intelligence)
maximum entropy methods
alternating direction method of multipliers
ADMM method
maximum correntropy criterion
outlier-insensitive machine learning
hyperspectral image unmixing
image decomposition
pixel linear combination
spectra linear combination
endmember extraction
nonnegativity constraints
sum-to-one constraints
signal-to-noise ratio
noise acquisition
atmospheric effects
Hyperspectral imaging
Robustness
Kernel
Optimization
Bandwidth
Linear programming
Correntropy
maximum correntropy estimation
hyperspectral image
unmixing problem
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