ENHANCING THE INTERPRETABILITY OF TERAHERTZ DATA THROUGH UNSUPERVISED CLASSIFICATION

Henrike Stephani, Michael Herrmann, Karin Wiesauer, Stefan Katletz, Bettina Heise

Abstract

We present the applicability of hierarchical agglomerative cluster algorithms to terahertz (THz) spectroscopic analysis. We show the influence of different windowing and filtering methods in the spectral data pre-processing to enhance the clustering results. Two distance measures are compared. Classical Euclidean distance on the full frequency range and a distance working only on the minima of the spectra. We further show the adaptability of our clustering methods for THz hyper-spectral image classification and visualization.

Keywords

clustering, THz Spectroscopy, THz Imaging

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IMEKO-WC-2009-TC21-637.pdf

DOI

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IMEKO TC

TC21 - Mathematical Tools for Measurements

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