Evaluation of PCA dimensionality reduction techniques in imaging spectroscopy for foreign object detection

Abstract

Foreign object detection processes are improving thanks to imaging spectroscopy techniques through the employment of hyperspectral systems such as prism-grating-prism spectrographs. These devices offer a valuable but sometimes huge and redundant amount of spectral and spatial information that facilitates and speed up the classification and sorting procedures of materials in industrial production chains. In this work, different algorithms of supervised and non-supervised Principal Components Analysis (PCA) are thoroughly applied on the experimentally acquired hyperspectral images. The evaluated PCA versions implement different statistical mechanisms to maximize the class separability. PCA alternatives (traditional m-method, J-measure, SEPCOR and Supervised PCA) are compared taking into account how the achieved spectral compression affects the classification performance in terms of accuracy and execution time. During the whole process, the classification stage is fixed and performed by an Artificial Neural Network (ANN). The developed techniques have been probed and successfully checked in tobacco industry where detection of plastics, cords, cardboards, papers, textile threads, etc. must be done in order to enter only tobacco leaves in the industrial chain.