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A Bayesian approach to identification of gaseous effluents in passive LWIR imagery

机译：贝叶斯方法识别被动LWIR图像中的废气

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Typically a regression approach is applied in order to identify the constituents present in a hyperspectral image, and the task of species identification amounts to choosing the best regression model. Common model selection approaches (stepwise and criterion based methods) have well known multiple comparisons problems, and they do not allow the user to control the experimet-wise error rate, or allow the user to include scene-specific knowledge in the inference process.rnA Bayesian model selection technique called Gibbs Variable Selection (GVS) that better handles these issues is presented and implemented via Markov chain monte carlo (MCMC). GVS can be used to simultaneously conduct inference on the optical path depth and probability of inclusion in a pixel for a each species in a library. This method flexibly accommodates an analyst's prior knowledge of the species present in a scene, as well as mixtures of species of any arbitrary complexity. A series of automated diagnostic measures are developed to monitor convergence of the Markov chains without operator intervention. This method is compared against traditional regression approaches for model selection and results from LWIR data from the Airborne Hyperspectral Imager (AHI) are presented. Finally, the applicability of this identification framework to a variety of scenarios such as persistent surveillance is discussed.
机译：通常，使用回归方法来识别高光谱图像中存在的成分，物种识别的任务在于选择最佳回归模型。常见的模型选择方法（基于逐步方法和基于标准的方法）具有众所周知的多重比较问题，并且它们不允许用户控制按经验计算的错误率，或者不允许用户在推理过程中包含特定于场景的知识。通过马尔可夫链蒙特卡洛（MCMC）提出并实现了一种称为Gibbs变量选择（GVS）的贝叶斯模型选择技术，该技术可以更好地解决这些问题。 GVS可用于同时对库中每个物种的光程深度和包含在像素中的概率进行推断。这种方法可以灵活地适应分析人员对场景中存在的物种以及任意复杂性的混合物的先验知识。开发了一系列自动诊断措施来监控马尔可夫链的收敛性，而无需操作员干预。将该方法与传统回归方法进行模型选择进行比较，并给出了机载高光谱成像仪（AHI）的LWIR数据的结果。最后，讨论了此识别框架在各种情况下（如持续监视）的适用性。

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