CC BY 4.0Bäcker, PaulPaulBäckerMaier, GeorgGeorgMaierGruna, RobinRobinGrunaLängle, ThomasThomasLängleBeyerer, JürgenJürgenBeyerer2023-04-042023-04-042023https://publica.fraunhofer.de/handle/publica/439476https://doi.org/10.24406/publica-118310.24406/publica-11832-s2.0-85153073931Polycyclic aromatic hydrocarbons (PAH) containing tar-mixtures pose a challenge for recycling road rubble, as the tar containing elements have to be extracted and decontaminated for recycling. In this preliminary study, tar, bitumen and minerals are discriminated using a combination of color (RGB) and Hyperspectral Short Wave Infrared (SWIR) cameras. Further, the use of an autoencoder for detecting minerals embedded inside tar- and bitumen mixtures is proposed. Features are extracted from the spectra of the SWIR camera and the texture of the RGB images. For classification, linear discriminant analysis combined with a k-nearest neighbor classification is used. First results show a reliable detection of minerals and positive signs for separability of tar and bitumen. This work is a foundation for developing a sensor-based sorting system for physical separation of tar contaminated samples in road rubble.enHyperspectral ImagingAutoencoderPolycyclic Aromatic Hydrocarbonsconference paper