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Enhancing the interpretability of terahertz data through unsupervised classification

: Stephani, H.; Herrmann, M.; Wiesauer, K.; Katletz, S.; Heise, B.

Volltext (PDF; )

Girao, P. ; International Measurement Confederation -IMEKO-, Budapest:
Fundamental and applied metrology. Proceedings of the XIX IMEKO world congress 2009. CD-ROM : September 6 - 11, 2009, Lisbon, Portugal
Lisbon, 2009
ISBN: 978-963-88410-0-1
IMEKO World Congress <19, 2009, Lisbon>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IPM ()
clustering; THz spectroscopy; THz imaging

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 preprocessing 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.