# Combined Use of 3D and HSI for the Classification of Printed Circuit Board Components
## 4 Discussion  
The results presented in Section 3 show significant added value of a combined use of 3D and HSI for classification of waste PCBs. In order to get the maximum benefit from such a combination, it is important for the strategic approach to analyze the underlying data sufficiently. A well-developed knowledge base with regard to the characteristics of objects helps to improve the results. Especially the addition of the 3D information is helpful in structuring and simplifying the datasets (background removal, removal of shadows, separation between objects). Hyperspectral data, on the other hand, help to determine the type of material and thus refines the classification. Even more important, each of the very different datasets supports the other and compensates the weaknesses of the other. Many of the components that are mounted on circuit boards have a black material color. The use of purely spectral information is not appropriate in these cases due to the high absorption of light. This is also confirmed by the SVM comparison (Table 2), where only HSI data were used for classification. A purely 3D-based classification can be successful in separating objects (e.g., board and components on board), but it would fail, for example, in differentiating between different board materials or compositions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **OA of Single** & **Error of Single** & **F1-Score** & **Kappa** \\ \hline PA & 0.9969 & 0.0030 & 0.9975 & 0.9584 \\ PP & 0.9961 & 0.0380 & 0.9966 & 0.8781 \\ PS & 0.9861 & 0.0138 & 0.9794 & 0.9723 \\ Board type 1 & 0.9075 & 0.0924 & 0.9327 & 0.7950 \\ Board type 2 & 0.9560 & 0.0439 & 0.9755 & 0.9628 \\ Board type 3 & 0.9689 & 0.0311 & 0.9789 & 0.9325 \\ ecob 1 & 0.8936 & 0.1063 & 0.9240 & 0.9947 \\ ecob 2 & 0.7206 & 0.2793 & 0.7594 & 0.9947 \\ ecob 3 & 0.7764 & 0.2235 & 0.7948 & 0.9970 \\ ecob 4 & 0.9849 & 0.0150 & 0.9094 & 0.9745 \\ ecob 5 & 0.8100 & 0.1899 & 0.7931 & 0.9848 \\ ecob 6 & 0.7114 & 0.2885 & 0.7127 & 0.9890 \\ ecob 7 & 0.6595 & 0.3404 & 0.5831 & 0.9981 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification accuracies for each class based on 3D and HSI data. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **(a)** & **(b)** \\
**Metrics** & **Rule-Based** & **SVM** \\  & **(3D + HSI)** & **(HSI)** \\ \hline Overall Accuracy & 0.9824 & 0.8865 \\ Precision & 0.8812 & 0.7812 \\ Sensitivity & 0.8834 & 0.6576 \\ False Positive Rate & 0.0023 & 0.0124 \\ F1-Score & 0.8810 & 0.6956 \\ Kappa & 0.8672 & 0.2011 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification accuracies for real electronic wastes using (a) Rule-based approach with combined 3D and HSI dataset; (b) SVM classification (C-SVC, RBF-Kernel, C: 2048, gamma: 2.8284) with HSI dataset. Especially with 3D data, the resolution and accuracy of the dataset used plays a major role. The higher the resolution, the more accurate the description of very fine, small objects on the board. We see in our experiments that we reach our limits with objects shown in Figure 16 and that a clean separation of small elements such as metals (red and blue box), resistors (green box) and capacitors (yellow box) is not successful. Nevertheless, even if a distinction between these elements does not seem possible in detail, these elements can be identified as components of PCBs through the given topological relationships, which could provide an advantage for further analyses (e.g., determination of different metals). The classification map shown in Figure 14 is the result of a total of 13 defined rules and works well for the materials and components in the used dataset. An extension to further objects and materials needs the exploitation of their characteristics and the definition of additional rules. This has to be completed carefully in order to avoid conflicting definitions and needs the existence of spatial and spectral characteristics allowing to differentiate further object types.