一种考虑端元变化和光谱突变的分层贝叶斯模型
A Hierarchical Bayesian Model Accounting for Endmember Variability and Abrupt Spectral Changes to Unmix Multitemporal Hyperspectral Images
摘要：
高光谱解混是一个盲源分离问题，它包括估计包含在高光谱图像中的参考光谱特征，以及它们根据给定的混合模型对每个像素的相对贡献。在实践中，该过程进一步复杂化的固有光谱变化的观察场景和可能存在的离群值。更具体地，多时间高光谱图像，即，在不同时刻在相同区域上获取的高光谱图像序列可能同时表现出适度的端元变化性和突然的光谱变化，这是由于离群值或连续获取之间的显著时间间隔造成的。除非适当考虑，这两个扰动可以显着影响的解混过程。在这种情况下，我们提出了一个新的解混模型的多时相高光谱图像占平稳的时间变化，解释为光谱变异性，和突然的光谱变化解释为离群值。建议的分层贝叶斯模型推断使用马尔可夫链蒙特卡罗（MCMC）方法，允许后验的兴趣进行采样和贝叶斯估计近似。从文献上的合成和真实数据的解混技术的比较允许的兴趣，所提出的方法被赞赏。
英文摘要：
Hyperspectral unmixing is a blind source separation problem which consists in estimating the reference spectral signatures contained in a hyperspectral image, as well as their relative contribution to each pixel according to a given mixture model. In practice, the process is further complexified by the inherent spectral variability of the observed scene and the possible presence of outliers. More specifically, multi-temporal hyperspectral images, i.e., sequences of hyperspectral images acquired over the same area at different time instants, are likely to simultaneously exhibit moderate endmember variability and abrupt spectral changes either due to outliers or to significant time intervals between consecutive acquisitions. Unless properly accounted for, these two perturbations can significantly affect the unmixing process. In this context, we propose a new unmixing model for multitemporal hyperspectral images accounting for smooth temporal variations, construed as spectral variability, and abrupt spectral changes interpreted as outliers. The proposed hierarchical Bayesian model is inferred using a Markov chain Monte-Carlo (MCMC) method allowing the posterior of interest to be sampled and Bayesian estimators to be approximated. A comparison with unmixing techniques from the literature on synthetic and real data allows the interest of the proposed approach to be appreciated.
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