Detection of explosives has always been a priority for homeland security. Jointly, terahertz spectroscopy and imaging are emerging and promising candidates as contactless and safe systems. In this work, we treated data resulting from hyperspectral imaging obtained by THz-time domain spectroscopy, with chemometric tools. We found efficient identification and sorting of targeted explosives in the case of pure and mixture samples. In this aim, we applied to images Principal Component Analysis (PCA) to discriminate between RDX, PETN and mixtures of the two materials, using the absorbance as the key-parameter. Then we applied Partial Least Squares-Discriminant Analysis (PLS-DA) to each pixel of the hyperspectral images to sort the explosives into different classes. The results clearly show successful identification and categorization of the explosives under study.
In this paper we present an innovative data and image processing sequence to perform non-destructive inspection from 3D terahertz (THz) images. We develop all the steps starting from a 3D tomographic reconstruction of a sample from its radiographs acquired with a monochromatic millimetre wave imaging system. Thus an automated segmentation provides the different volumes of interest (VOI) composing the sample. Then a 3D visualization and dimensional measurements are performed on these VOI, separately, in order to provide an accurate nondestructive testing (NDT) of the studied sample. This sequence is implemented onto an unique software and validated through the analysis of different objects
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