Selecting a representative training set for the classification of demolition waste using remote NIR sensing Original Research Article

In the AUTOSORT project, the goal is the separation of demolition waste in three fractions: wood, plastics and stone. A remote near-infrared sensor measures reduced reflectance spectra (mini-spectra) of objects. Linear discriminant analysis (LDA) is used for the classification of these spectra. To obtain the LDA model, a representative training set is needed. New LDA-models will be regularly needed for recalibrations. Small training sets will save a lot of labour and additional costs. Two object selection methods are investigated: the Kennard–Stone algorithm and a statistical test procedure. Training sets are acquired from which the mini-spectra are used to obtain LDA models. In the training sets, the object amounts and their ratios are varied. Two object ratios are applied: the ratios as they occur in the complete data set and the equalised ratios. The Kennard–Stone selection algorithm is the preferred method. It gives a unique list of objects, mainly sampled at the cluster borders: partial cluster overlap is better defined. This is in contradiction with the sets of objects, accepted by the statistical test procedure: those objects tend to occur around the fraction means. This is a drawback for the classification performance: some accepted training sets are unacceptable. The ratios between the fraction amounts are not important, but equal fraction amounts are preferred. Selecting 25 objects for each fraction should be suitable.