Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing

TL;DR

This paper introduces a robust hyperspectral unmixing method using correntropy maximization solved via ADMM, effectively handling noisy and outlier spectral bands in hyperspectral images.

Contribution

It presents a novel correntropy-based unmixing framework with ADMM optimization for robustness against noise and outliers, including fully-constrained and sparse variants.

Findings

Demonstrates robustness to outlier bands in synthetic and real data

Outperforms traditional methods in noisy scenarios

Efficient ADMM-based optimization implementation

Abstract

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 efficiently 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 is robust to outlier bands.