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Detection of unknown gas-phase chemical plumes in hyperspectral imagery

机译:高光谱图像中未知气相化学羽流的检测

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Gas-phase chemical plumes exhibit, particularly in the infrared, distinctive emission signatures as a function of wavelength. Hyperspectral imagery can exploit this distinctiveness to detect specific chemicals, even at low concentrations, using matched filters that are tailored both the the specific structure of the chemical signature and to the statistics of the background clutter. But what if the chemical species is unknown? One can apply matched filters to a long list of candidate chemicals (or chemical mixtures), or one can treat the problem as one of anomaly detection. In this case, however, the anomalous signals of interest are not completely unknown. Gas spectra are generically sparse (absorbing or emitting at only a few wavelengths), and this property can be exploited to enhance the sensitivity of anomaly detection algorithms. This paper investigates the utility of sparse signal anomaly detection for the problem of finding plumes of gas with unknown chemistry in hyperspectral imagery.
机译:气相化学羽流,特别是在红外线中,表现出与波长有关的独特发射特征。高光谱图像可以利用匹配的滤光片来利用这种独特性来检测特定的化学物质,即使在低浓度下也是如此,这些滤光片可以针对化学特征的特定结构和背景杂波的统计量身定制。但是,如果化学物种未知,该怎么办?一个人可以将匹配的过滤器应用于一长串候选化学物质(或化学混合物),或者可以将问题视为异常检测之一。然而,在这种情况下,感兴趣的异常信号并不是完全未知的。气相色谱通常是稀疏的(仅在几个波长处吸收或发射),并且可以利用此属性来增强异常检测算法的灵敏度。本文研究稀疏信号异常检测在寻找高光谱图像中化学成分未知的气体羽流问题中的实用性。

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