Successful recycling of electronic waste requires accurate separation of materials such as plastics, PCBs and electronic components on PCBs (capacitors, transistors, etc.). This article therefore proposes a vision approach based on a combination of 3D and HSI data, relying on the mutual support of the datasets to compensate existing weaknesses when using single 3D- and HSI-Sensors. The combined dataset serves as a basis for the extraction of geometric and spectral features. The classification is performed and evaluated based on these extracted features which are exploited through rules. The efficiency of the proposed approach is demonstrated using real electronic waste and leads to convincing results with an overall accuracy (OA) of 98.24%. To illustrate that the addition of 3D data has added value, a comparison is also performed with an SVM classification based only on hyperspectral data.

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Due to its high spatial and spectral information content, hyperspectral imaging opens up new possibilities for a better understanding of data and scenes in a wide variety of applications. An essential part of this process of understanding is the classification part. However, the high spatial and spectral resolution also leads to enormous amounts of data. The effective handling and use of such datasets for classification requires processing steps (dimensionality reduction through feature selection or feature extraction) that are not always goal-oriented. In this article, a new general classification approach is presented that uses the geometric shape of spectral signatures instead of purely statistical methods. In contrast to classical classification approaches (e.g., SVM, KNN), not only are reflectance values taken into account, but also parameters such as curvature points, curvature values, and the curvature behavior of spectral signatures are used to develop shape-describing rules in order to use them for classification by a rule-based procedure with IF-THEN queries. The flexibility and efficiency of the methodology are demonstrated on datasets from two different application domains and lead to convincing results with good performance.