ENHANCING THE INTERPRETABILITY OF TERAHERTZ DATA THROUGH UNSUPERVISED CLASSIFICATION

• 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
• Download:

  IMEKO-WC-2009-TC21-637.pdf
• DOI:

  _unreg_wc-2009.637

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