Spectral Unmixing of Multispectral Lidar Signals
Spectral Unmixing of Multispectral Lidar Signals
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DOI：
10.1109/tsp.2015.2457401
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发表时间：
2015-01
影响因子：
5.4
通讯作者：
Y. Altmann;A. Wallace;S. Mclaughlin
中科院分区：
文献类型：
--
作者：
Y. Altmann;A. Wallace;S. Mclaughlin
本文提出了一种贝叶斯方法对目标表面反射的多光谱激光雷达(MSL)数据进行光谱分解。所解决的问题是对每种材料的位置和面积分布的估计。在贝叶斯框架下，将适当的先验分布分配给未知的模型参数，并使用马尔科夫链蒙特卡罗方法对得到的后验分布进行采样。利用合成MSL信号对该算法的性能进行了评估，并建立了单层和多层模型。为了评估与MSL信号分析相关的期望估计性能，还推导了与所考虑的模型相关的Cramer-Rao下界，并与实验数据进行了比较。理论下限和实验分析都将为今后的仪器设计提供初步的帮助。
In this paper, we present a Bayesian approach for spectral unmixing of multispectral Lidar (MSL) data associated with surface reflection from targeted surfaces composed of several known materials. The problem addressed is the estimation of the positions and area distribution of each material. In the Bayesian framework, appropriate prior distributions are assigned to the unknown model parameters and a Markov chain Monte Carlo method is used to sample the resulting posterior distribution. The performance of the proposed algorithm is evaluated using synthetic MSL signals, for which single and multi-layered models are derived. To evaluate the expected estimation performance associated with MSL signal analysis, a Cramer-Rao lower bound associated with model considered is also derived, and compared with the experimental data. Both the theoretical lower bound and the experimental analysis will be of primary assistance in future instrument design.