使用扰动线性混合模式的具有光谱变率的高光谱解混
Hyperspectral unmixing with spectral variability using a perturbed linear mixing model
摘要：
给定一个混合的高光谱数据集，线性分解的目的是估计组成数据的参考光谱特征-称为端元-它们的丰度分数和它们的数量。在实践中，所识别的端元可以在给定图像内光谱地变化，并且因此可以被解释为参考端元的可变实例。忽略该可变性会引起传播到解混过程中的估计误差。为了解决这个问题，端元变异性估计包括估计的参考光谱特征，估计的端元已经得到，以及他们的变异性相对于这些参考。本文介绍了一种新的线性混合模型，明确占空间和光谱端元变异。该模型的参数可以使用基于乘子交替方向法的优化算法来估计。所提出的解混方法的性能进行评估合成和真实数据。与设计用于建模和估计端元可变性的最新算法的比较允许对所提出的解混解决方案的兴趣进行理解。
英文摘要：
Given a mixed hyperspectral data set, linear unmixing aims at estimating the reference spectral signatures composing the data - referred to as endmembers - their abundance fractions and their number. In practice, the identified endmembers can vary spectrally within a given image and can thus be construed as variable instances of reference endmembers. Ignoring this variability induces estimation errors that are propagated into the unmixing procedure. To address this issue, endmember variability estimation consists of estimating the reference spectral signatures from which the estimated endmembers have been derived as well as their variability with respect to these references. This paper introduces a new linear mixing model that explicitly accounts for spatial and spectral endmember variabilities. The parameters of this model can be estimated using an optimization algorithm based on the alternating direction method of multipliers. The performance of the proposed unmixing method is evaluated on synthetic and real data. A comparison with state-of-the-art algorithms designed to model and estimate endmember variability allows the interest of the proposed unmixing solution to be appreciated.
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