Automatic Identification of Rock-Forming Minerals in Granite Using Laboratory Scale Hyperspectral Reflectance Imaging and Artificial Neural Networks

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Laser cleaning of stones is a well-established technique in conservation of Cultural Heritage. In case of polymineralic granular rocks like granite, the tuning of the laser parameters for safe cleaning becomes a demanding task due to the differential response of the constituent minerals to laser irradiation. In this sense, a compromise solution must be reached between removing unwanted layers and safeguarding the original characteristics of the stone substrate. Consequently, the need of non-invasive, rapid and accurate methods for in situ identification of the minerals in the stone surface is highlighted. The aim of this paper is to demonstrate the ability of a laboratory scale hyperspectral reflectance imaging system, in combination with artificial neural networks for in situ, accurate identification of constituent minerals of a hercynian granite; with the ultimate goal of achieving automatic adjustment of laser irradiation parameters in cleaning processes. The effectiveness of a neural network with the structure of a three-layer perceptron was evaluated by comparing calculated results with modal analysis composition obtained by petrographic microscope and a high degree of accordance, above 95%, was achieved.