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Terahertz (THz) technology has been a great candidate for applications,including pharmaceutic analysis, chemical identification, and remote sensingand imaging due to its non-invasive and non-destructive properties. Among thoseapplications, penetrating-type hyperspectral THz signals, which provide crucialmaterial information, normally involve a noisy, complex mixture system.Additionally, the measured THz signals could be ill-conditioned due to theoverlap of the material absorption peak in the measured bands. To address thoseissues, we consider penetrating-type signal mixtures and aim to develop a blindhyperspectral unmixing (HU) method without requiring any information from aprebuilt database. The proposed HYperspectral Penetrating-type EllipsoidalReconstructION (HYPERION) algorithm is unsupervised, not relying on collectingextensive data or sophisticated model training. Instead, it is developed basedon elegant ellipsoidal geometry under a very mild requirement on data purity,whose excellent efficacy is experimentally demonstrated.

HYPERION: Hyperspectral Penetrating-type Ellipsoidal Reconstruction for Terahertz Blind Source Separation