MULTI-LABEL CLASSIFICATION FOR DRILL-CORE HYPERSPECTRAL MINERAL MAPPING
Keywords: mineral mapping, drill-core hyperspectral data, mineral liberation analysis, classifier chains, random forest, multi-label classification, machine learning
Abstract. A multi-label classification concept is introduced for the mineral mapping task in drill-core hyperspectral data analysis. As opposed to traditional classification methods, this approach has the advantage of considering the different mineral mixtures present in each pixel. For the multi-label classification, the well-known Classifier Chain method (CC) is implemented using the Random Forest (RF) algorithm as the base classifier. High-resolution mineralogical data obtained from Scanning Electron Microscopy (SEM) instrument equipped with the Mineral Liberation Analysis (MLA) software are used for generating the training data set. The drill-core hyperspectral data used in this paper cover the visible-near infrared (VNIR) and the short-wave infrared (SWIR) range of the electromagnetic spectrum. The quantitative and qualitative analysis of the obtained results shows that the multi-label classification approach provides meaningful and descriptive mineral maps and outperforms the single-label RF classification for the mineral mapping task.