  Given the hyper-spectral imaging unique potentials in grasping the polymer
characteristics of different materials, it is commonly used in sorting
procedures. In a practical plastic sorting scenario, multiple plastic flakes
may overlap which depending on their characteristics, the overlap can be
reflected in their spectral signature. In this work, we use hyper-spectral
imaging for the segmentation of three types of plastic flakes and their
possible overlapping combinations. We propose an intuitive and simple
multi-label encoding approach, bitfield encoding, to account for the
overlapping regions. With our experiments, we show that the bitfield encoding
improves over the baseline single-label approach and we further demonstrate its
potential in predicting multiple labels for overlapping classes even when the
model is only trained with non-overlapping classes.
