考虑端元变化的高光谱图像非监督解混
Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability
摘要：
提出了一种考虑端元变化的无监督贝叶斯高光谱图像解混算法。像素由端元的线性组合建模，端元由其相应的丰度加权。然而，假设端元是随机的，以考虑它们在图像中的变化。加性噪声也被认为是在所提出的模型推广正常的组成模型。该算法利用整个图像提供光谱和空间信息。它估计图像中每个端元的均值和协方差阵。这允许分析每个材质的行为，并在场景中量化其可变性。基于估计的丰度还获得空间分割。为了估计与所提出的贝叶斯模型相关的参数，我们建议使用哈密顿蒙特卡罗算法。通过对合成数据和真实数据进行模拟来评估所得到的解混策略的性能。
英文摘要：
This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember variability. The pixels are modeled by a linear combination of endmembers weighted by their corresponding abundances. However, the endmembers are assumed random to take into account their variability in the image. An additive noise is also considered in the proposed model generalizing the normal compositional model. The proposed algorithm exploits the whole image to provide spectral and spatial information. It estimates both the mean and the covariance matrix of each endmember in the image. This allows the behavior of each material to be analyzed and its variability to be quantified in the scene. A spatial segmentation is also obtained based on the estimated abundances. In order to estimate the parameters associated with the proposed Bayesian model, we propose to use a Hamiltonian Monte Carlo algorithm. The performance of the resulting unmixing strategy is evaluated via simulations conducted on both synthetic and real data.
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