Automated ore microscopy based on multispectral measurements of specular reflectance. I - A comparative study of some supervised classification techniques

摘要

Automated mineralogy, including quantitative compositional and textural information, is a requirement for an efficient ore processing, and is comprised as an important input for geometallurgical planning. Classical ore microscopy is seen by many potential users as an outdated, time consuming, tool. Thus, SEM-based systems are often the choice for those who can afford them, in spite of their evident limitations for some minerals (e.g. for iron oxide ores). However, automated and quantitative mineral characterisation of metallic ores is also possible with optical (reflected light) microscopes, attaining similar performance at a much lower price than SEM systems, as shown by the AMCO (Automated Microscopic Characterisation of Ores) prototype. This system relies on the measurement of multispectral specular reflectance, R, on polished ore sections, to achieve automated identification of the ore species (reflectance is computed from grey levels of digital images acquired by automated scanning of the sample). The performance of this approach, supported by a multispectral reflectance database covering the VNIR range (370-1000 nm) built with the AMCO System, is analysed in this paper, comparing the reliability of different classification methods to achieve ore identification.