PLASTIC IDENTIFICATION BY REMOTE-SENSING SPECTROSCOPIC NIR IMAGING USING KERNEL PARTIAL LEAST-SQUARES (KPLS)

This work describes the application of partial least squares (PLS) mod eling in data reduction purposes for the classification of spectroscop ic near infrared (NIR) images. Given multi-dimensional images (i.e. p images taken at p different wavelengths regions in the NIR-range), PLS projects the (nearly void) high dimensional space into a low dimensio nal latent space using the coded class information of the sample objec ts. Hence, PLS can be considered as a supervised latent variable analy sis. In addition, data reduction by PLS increases the speed of on-line classification which is attractive in, e.g., process control. In orde r to apply these conditions on imaging problems a rapid PLS version, k ernel 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 appli cation entails the discrimination between the materials plastics, non- plastics and image backgrounds.