基于贝叶斯源分离的高光谱解混实现策略
Implementation strategies for hyperspectral unmixing using Bayesian source separation
摘要：
贝叶斯正源分离（BPSS）是一种有用的无监督的高光谱数据分解方法，其中光谱和丰度的数值非负性必须得到保证，例如在遥感。此外，对每个像素中的估计源丰度施加总和为一（全加性）约束是明智的。尽管非负性和完全可加性是获得物理上可解释的结果的两个必要属性，但由于马尔可夫链蒙特卡罗计算，BPSS算法的使用迄今为止受到高计算时间和大内存需求的限制。一个实施策略，它允许一个应用这些算法在一个完整的高光谱图像，典型的地球和行星科学，介绍。像素选择的影响，这样的采样的相关性的估计成分谱和丰度图，以及计算时间的影响，进行了讨论。为此目的，使用了两个不同的数据集：一个是合成数据集，另一个是来自火星的真实高光谱图像。
英文摘要：
Bayesian Positive Source Separation (BPSS) is a useful unsupervised approach for hyperspectral data unmixing, where numerical non-negativity of spectra and abundances has to be ensured, such in remote sensing. Moreover, it is sensible to impose a sum-to-one (full additivity) constraint to the estimated source abundances in each pixel. Even though non-negativity and full additivity are two necessary properties to get physically interpretable results, the use of BPSS algorithms has been so far limited by high computation time and large memory requirements due to the Markov chain Monte Carlo calculations. An implementation strategy which allows one to apply these algorithms on a full hyperspectral image, as typical in Earth and Planetary Science, is introduced. Effects of pixel selection, the impact of such sampling on the relevance of the estimated component spectra and abundance maps, as well as on the computation times, are discussed. For that purpose, two different dataset have been used: a synthetic one and a real hyperspectral image from Mars.
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