Wavelet-based dimensionality reduction for hyperspectral THz imaging

With terahertz time-domain spectroscopy, hyperspectral images can be acquired where each pixel contains a full spectrum of the range of several terahertz (THz). An enormous amount of data is generated. Therefore, advanced methods for automated data analysis and image processing are required. We present a wavelet-based approach for channel reduction and feature selection for a subsequent clustering leading to an image segmentation. The main focus of our method is set on the appropriate dimensionality reduction adapted to the THz spectral characteristics of the samples under investigation. A feature reduction to less than 5% is achieved, thereby enabling a channel-wise image processing on the reduced data set. Furthermore, unsupervised classification is chosen for an automatized segmentation including all channel information represented in the wavelet domain. Relevant characteristics of the THz spectra are preserved by our feature selection, in particular the distribution of the peak position and peak depth. The proposed method for channel reduction is verified by extensive simulations at first. Finally, it is demonstrated on various real-world measurements of chemical compounds. The improved performance of the analysis on the reduced feature set could be shown in comparison with the evaluation on the full data set.