Mineral identification using artificial neural networks and the rotating polarizer stage

An artificial neural network is used for the classification of minerals. Op tical data using thin sections is acquired using the rotating polarizing mi croscope stage, which extracts a basic set of seven primary images during e ach sampling. A selected set of parameters based on hue, saturation, intens ity and texture measurements are extracted from the segmented minerals with in each data set. Parameters such as pleochroism, plane light hue, and grad ient homogeneity were a few that proved to yield class-discriminating prope rties. Texture parameters are shown to have the ability to classify colourl ess minerals. The neural network is trained on manually classified mineral samples. The most successful artificial network to date is a three-layer fe ed forward network using generalized delta error correction. The network us es 27 distinct input parameters to classify 10 different minerals. Testing the network on previously unseen mineral samples yielded successful results as high as 93%. (C) 2001 Elsevier Science Ltd. All rights reserved.