高光谱图像贝叶斯端元提取与线性解混
Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery
摘要：
本文研究了一种基于全贝叶斯的高光谱图像端元提取和丰度估计算法。高光谱图像的每个像素被分解为纯端元光谱的线性组合，遵循线性混合模型。未知端元谱的估计是在一个统一的方式进行的，通过生成的后验分布的丰度和端元参数下的分层贝叶斯模型。该模型假设这些参数的共轭先验分布，考虑非负性和全加性约束，并利用端元比例位于低维单纯形上的事实。吉布斯采样器，提出了克服复杂的评估所产生的后验分布。该采样器生成根据后验分布的样本，并使用这些生成的样本估计未知参数。联合贝叶斯估计的准确性说明合成和真实AVIRIS图像进行模拟。
英文摘要：
This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing model. The estimation of the unknown endmember spectra is conducted in a unified manner by generating the posterior distribution of abundances and endmember parameters under a hierarchical Bayesian model. This model assumes conjugate prior distributions for these parameters, accounts for non-negativity and full-additivity constraints, and exploits the fact that the endmember proportions lie on a lower dimensional simplex. A Gibbs sampler is proposed to overcome the complexity of evaluating the resulting posterior distribution. This sampler generates samples distributed according to the posterior distribution and estimates the unknown parameters using these generated samples. The accuracy of the joint Bayesian estimator is illustrated by simulations conducted on synthetic and real AVIRIS images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
