Abstract. Trace chemical detection and classification in stand-off reflection-based spectroscopic data is challenging due to the variability of measured data and the lack of physics-based models that can accurately predict spectra. Most available models assume that the chemical takes the form of spherical particles or uniform thin films. A more realistic chemical presentation that could be encountered is that of a nonuniform chemical film that is deposited after evaporation of the solvent that contained the chemical. We present an improved signature model for this type of solid film. The proposed model, called sparse transfer matrix, includes a log-normal distribution of film thicknesses and is found to reduce the root mean square error between simulated and measured data by about 25% when compared with either the particle or uniform thin film models. When applied to measured data, the sparse transfer matrix model provides a 10% to 28% increase in classification accuracy over traditional models.

中文翻译：

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在主动光谱测量中改进表面痕量化学残留物分类的实用模型

摘要。由于测量数据的可变性以及缺乏可以准确预测光谱的基于物理的模型，因此基于远距离反射的光谱数据中的痕量化学检测和分类具有挑战性。大多数可用模型假设化学物质采用球形颗粒或均匀薄膜的形式。可能遇到的更现实的化学表现是在含有化学物质的溶剂蒸发后沉积的不均匀化学膜。我们为这种类型的固体薄膜提供了一个改进的签名模型。提出的模型，称为稀疏转移矩阵，包括薄膜厚度的对数正态分布，并且与颗粒或均匀薄膜模型相比，可以将模拟数据和测量数据之间的均方根误差降低约 25%。当应用于测量数据时，稀疏传递矩阵模型比传统模型提高了 10% 到 28% 的分类准确度。