Plastic identification by remote sensing spectroscopic NIR imaging using kernel partial least squares (KPLS)

This work describes the application of partial least squares (PLS) modeling in data reduction purposes for the classification of spectroscopic near infrared (NIR) images. Given multi-dimensional images (i.e. p images taken at p different wave-lengths regions in the NIR-range), PLS projects the (nearly void) high dimensional space into a low dimensional latent space using the coded class information of the sample objects. Hence, PLS can be considered as a supervised latent variable analysis. In addition, data reduction by PLS increases the speed of on-line classification which is attractive in, e.g., process control. In order to apply these conditions on imaging problems a rapid PLS version, kernel PLS, is investigated. Emphasis is put on the performance of PLS as a supervised data decomposition technique for the classification of collinear image data, applied on a real world application. This application entails the discrimination between the materials plastics, non-plastics and image backgrounds.