通过ADMM的相关熵最大化-应用于鲁棒高光谱解混
Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing
摘要：
在高光谱图像中，一些光谱波段由于噪声采集和大气影响而遭受低信噪比，因此需要鲁棒的技术来解决混合问题。提出了一种稳健的高光谱图像监督光谱分解方法。鲁棒性是通过将解混问题写为受最常用约束的相关熵准则的最大化来实现的。推导出两个解混合问题：第一个问题考虑完全约束解混合，具有非负性和和对一约束，而第二个问题处理丰度的非负性和稀疏性提升。相应的优化问题有效地解决了交替方向的乘法器（ADMM）的方法。合成和真实高光谱图像上的实验验证了所提出的算法在不同场景下的性能，表明基于相关性的解混对离群波段具有鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
