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

Abstract

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.