Subspace clustering algorithms for mineral mapping

The application of drill core hyperspectral data in exploration campaigns is receiving great interest to obtain a general overview of a mineral deposit. However, the main approach to the investigation of such data is by visual interpretation, which is subjective and time-consuming. To address this issue, recently, the use of machine learning techniques is proposed for the analysis of this data. For drill core samples that for which only very little prior knowledge is often available, applying classification algorithms which are supervised learning methods is very challenging. In this paper, we suggest to use clustering (unsupervised) methods for mineral mapping, which are similar to classification but no predefined class labels are needed. To handle mapping of the very highly mixed pixels in drill core hyperspectral data, we propose to use advanced subspace clustering methods, in which pixels are assumed to lie in a union of low-dimensional subspaces. We conduct a comparative study and evaluate the performance of two well-known subspace clustering methods, i.e., sparse subspace clustering (SSC) and low-rank representation (LRR). For the experiments, we acquired VNIR-SWIR hyperspectral data and applied scanning electron microscopy based Mineral Liberation Analysis (MLA) for two drill core samples. MLA is a high-resolution imaging technique that allows detailed mineral characterization. We use the high-resolution MLA image as a reference to analyze the clustering results. Qualitative analysis of the obtained clustering maps indicates that the subspace clustering methods can accurately map the available minerals in the drill core hyperspectral data, especially in comparison to the traditional k-means clustering method.