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首页> 外文会议>Fifth International Conference on Optical and Photonics Engineering >Snapshot hyperspectral imaging probe with principal component analysis and confidence ellipse for classification
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Snapshot hyperspectral imaging probe with principal component analysis and confidence ellipse for classification

机译：具有主成分分析和置信椭圆的快照高光谱成像探头，用于分类

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Hyperspectral imaging combines imaging and spectroscopy to provide detailed spectral information for each spatial point in the image. This gives a three-dimensional spatial-spatial-spectral datacube with hundreds of spectral images. Probe-based hyperspectral imaging systems have been developed so that they can be used in regions where conventional table-top platforms would find it difficult to access. A fiber bundle, which is made up of specially-arranged optical fibers, has recently been developed and integrated with a spectrograph-based hyperspectral imager. This forms a snapshot hyperspectral imaging probe, which is able to form a datacube using the information from each scan. Compared to the other configurations, which require sequential scanning to form a datacube, the snapshot configuration is preferred in real-time applications where motion artifacts and pixel misregistration can be minimized. Principal component analysis is a dimension-reducing technique that can be applied in hyperspectral imaging to convert the spectral information into uncorrelated variables known as principal components. A confidence ellipse can be used to define the region of each class in the principal component feature space and for classification. This paper demonstrates the use of the snapshot hyperspectral imaging probe to acquire data from samples of different colors. The spectral library of each sample was acquired and then analyzed using principal component analysis. Confidence ellipse was then applied to the principal components of each sample and used as the classification criteria. The results show that the applied analysis can be used to perform classification of the spectral data acquired using the snapshot hyperspectral imaging probe.
机译：高光谱成像结合了成像和光谱学，可为图像中的每个空间点提供详细的光谱信息。这给出了具有数百个光谱图像的三维空间空间光谱数据立方体。已经开发了基于探针的高光谱成像系统，因此它们可以用于传统台式平台难以访问的区域。最近开发了一种由特殊排列的光纤组成的光纤束，并将其与基于光谱仪的高光谱成像仪集成在一起。这形成了快照高光谱成像探针，它能够使用来自每次扫描的信息来形成数据立方体。与需要顺序扫描以形成数据多维数据集的其他配置相比，快照配置在实时应用中是首选的，在这些应用中可以最大程度地减少运动伪像和像素重合失调。主成分分析是一种降维技术，可用于高光谱成像，以将光谱信息转换为不相关的变量，称为主成分。置信椭圆可用于定义主成分特征空间中每个类别的区域并用于分类。本文演示了快照高光谱成像探针如何从不同颜色的样本中获取数据。采集每个样品的光谱库，然后使用主成分分析进行分析。然后将置信椭圆应用于每个样本的主要成分，并用作分类标准。结果表明，所应用的分析可用于对使用快照高光谱成像探头获得的光谱数据进行分类。

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