Browsing by Author "Bortolussi, Federica"

The exploration of mineral resources is a major challenge in a world that seeks sustainable energy, renewable energy, advanced engineering, and new commercial technological devices. The rapid decrease in mineral reserves shifted the focus to under-explored and low accessibility areas that led to the use of on-site portable techniques for mineral mapping purposes, such as near infrared hyperspectral image sensors. The large datasets acquired with these instruments needs data pre-processing, a series of mathematical manipulations that can be achieved using machine learning. The aim of this thesis is to improve an existing method for mineralogy mapping, by focusing on the mineral classification phase. More specifically, a spectral similarity index was utilized to support machine learning classifiers. This was introduced because of the inability of the employed classification models to recognize samples that are not part of a given database; the models always classified samples based on one of the known labels of the database. This could be a problem in hyperspectral images as the pure component found in a sample could correspond to a mineral but also to noise or artefacts due to a variety of reasons, such as baseline correction. The spectral similarity index calculates the similarity between a sample spectrum and its assigned database class spectrum; this happens through the use of a threshold that defines whether the sample belongs to a class or not. The metrics utilized in the spectral similarity index were the spectral angler mapper, the correlation coefficient and five different distances. The machine learning classifiers used to evaluate the spectral similarity index were the decision tree, k-nearest neighbor, and support vector machine. Simulated distortions were also introduced in the dataset to test the robustness of the indexes and to choose the best classifier. The spectral similarity index was assessed with a dataset of nine minerals acquired from the Geological Survey of Finland retrieved from a Specim SWIR camera. The validation of the indexes was assessed with two mine samples obtained with a VTT active hyperspectral sensor prototype. The support vector machine was chosen after the comparison between the three classifiers as it showed higher tolerance to distorted data. With the evaluation of the spectral similarity indexes, was found out that the best performances were achieved with SAM and Chebyshev distance, which maintained high stability with smaller and bigger threshold changes. The best threshold value found is the one that, in the dataset analysed, corresponded to the number of spectra available for each class. As for the validation procedure no reference was available; because of this reason, the results of the mine samples obtained with the spectral similarity index were compared with results that can be obtained through visual interpretation, which were in agreement. The method proposed can be useful to future mineral exploration as it is of great importance to correctly classify minerals found during explorations, regardless the database utilized.