Robust Hyperspectral Unmixing with Correntropy based Metric
This work addresses a challenging domain-specific problem in hyperspectral imaging for applications like remote sensing, but it is incremental as it builds on existing unmixing models with a new metric and constraints.
The paper tackles the problem of robust unsupervised hyperspectral unmixing in the presence of noisy spectral bands, using a correntropy-based metric with sparsity constraints, and demonstrates its effectiveness compared to state-of-the-art models on synthetic and real data.
Hyperspectral unmixing is one of the crucial steps for many hyperspectral applications. The problem of hyperspectral unmixing has proven to be a difficult task in unsupervised work settings where the endmembers and abundances are both unknown. What is more, this task becomes more challenging in the case that the spectral bands are degraded with noise. This paper presents a robust model for unsupervised hyperspectral unmixing. Specifically, our model is developed with the correntropy based metric where the non-negative constraints on both endmembers and abundances are imposed to keep physical significance. In addition, a sparsity prior is explicitly formulated to constrain the distribution of the abundances of each endmember. To solve our model, a half-quadratic optimization technique is developed to convert the original complex optimization problem into an iteratively re-weighted NMF with sparsity constraints. As a result, the optimization of our model can adaptively assign small weights to noisy bands and give more emphasis on noise-free bands. In addition, with sparsity constraints, our model can naturally generate sparse abundances. Experiments on synthetic and real data demonstrate the effectiveness of our model in comparison to the related state-of-the-art unmixing models.