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首页> 外文学位 >A Bayesian approach to identification of gaseous effluents in passive LWIR imagery.
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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图像中的气体流出物。

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摘要

Typically a regression approach is applied in order to identify the gaseous 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 experiment-wise error rate, or allow the user to include scene-specific knowledge in the inference process.;A 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 modified version of GVS with fast convergence properties that is tailored to unsupervised use in hyperspectral image analysis will be presented. Additionally a series of automated diagnostic measures have been developed to monitor convergence of the MCMC with minimal operator intervention. Finally, the applicability of aggregating inference from adjacent pixels will be discussed.;This method is compared against stepwise regression for model selection and results from LWIR data from the Airborne Hyperspectral Imager (AHI) are presented. Finally, the applicability of this method to operational scenarios and various sensors will be discussed.
机译：通常，使用回归方法来识别高光谱图像中存在的气态成分，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实验方法的错误率，或者不允许用户在推理过程中包括特定于场景的知识。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所存在物种的先验知识，以及任意复杂程度的物种混合物。;将提供具有快速收敛特性的GVS修改版，该修改版适用于无监督地用于高光谱图像分析中。另外，已经开发了一系列自动诊断措施，以最少的操作员干预来监视MCMC的收敛。最后，将讨论从相邻像素进行汇总推断的适用性。；将该方法与逐步回归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，将讨论该方法对操作场景和各种传感器的适用性。

著录项

  • 作者

    Higbee, Shawn.;

  • 作者单位

    Rochester Institute of Technology.;

  • 授予单位 Rochester Institute of Technology.;
  • 学科 Remote Sensing.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 167 p.
  • 总页数 167
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 遥感技术;
  • 关键词

  • 入库时间 2022-08-17 11:38:29

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