温度发射率分离:使用影响观测值均值和方差的参数进行估计

T. Moon, D. A. Neal, J. Gunther, G. Williams
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引用次数: 3

摘要

我们考虑了高光谱图像处理中的温度发射率分离(TES)模型。发射率受黑体函数和大气下流的调制。这种相互作用使得同时提取温度和发射率变得困难,因为其中一个的偏移可以由另一个来补偿。仅处理单个波长分量,我们在这里提出了一个模型,其中下行被认为是一个随机变量(或矢量)。因此,发射率对观测值的方差和平均值都有贡献。这就得到了辐射率的最大似然估计。我们计算了这个估计量的偏置表达式,并展示了如何用它来产生无偏估计量。给出了温度的估计量。这两个估计器可以迭代使用,提供温度和发射率分量的分离。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Temperature emissivity separation: Estimation with a parameter affecting both the mean and variance of the observation
We consider a model for temperature-emissivity separation (TES) in hyperspectral image processing. The emissivity is modulated by both the black body function and the atmospheric downwelling. The interaction has made it difficult to extract both temperature and emissivity, since offsets in one can be compensated by the other. Working with only a single wavelength component, we propose here a model in which the downwelling is considered as a random variable (or vector). The emissivity thus contributes to both the variance and mean of the observations. This leads to a maximum likelihood estimator for the emissivity. We compute an expression for the bias of this estimator, and show how it can be used to produce an unbiased estimator. An estimator for the temperature is also given. These two estimators can be used iteratively, providing separation of the temperature and emissivity components.
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