Unsupervised Material Classification of Printed Circuit Boards Using Dimension-Reduced Spectral Information

This paper proposes an unsupervised method for material classification of raw printed circuit boards (PCBs) based on the dimension-reduced spectral information. First, a spectral imaging system is constructed for acquiring precise spectral reflectance images. Only 3-dimensional spectral features are then extracted from the original 31-dimensional spectral image data by PCA for segmentation proposes. Next we apply the normalized cut and k-means clustering algorithms to the material classification using the reduced spectral reflectance data. Experimental results show the feasibility of the proposed method in comparison with the relevant 3-dimensional RGB imaging system. It is shown that the proposed method achieves very high quality in the material classification.