Publications
Multispectral rock-type separation and classification
This paper explores the possibility of separating and classifying remotely-sensed multispectral data from rocks and minerals onto seven geological rock-type groups. These groups are extracted from the general categories of metamorphic, igneous and sedimentary rocks. The study is performed under ideal conditions for which the data is generated according to laboratory hyperspectral data for the members, which are, in turn, passed through the Multi-spectral Thermal Imager (MTI) filters yielding 15 bands. The main challenge in separability is the small size of the training data sets, which initially did not permit direct application of Bayesian decision theory. To enable Bayseian classification, the original training data is linearly perturbed with the addition minerals, vegetation, soil, water and other valid impurities. As a result, the size of the training data is significantly increased and accurate estimates of the covariance matrices are achieved. In addition, a set of reduced (five) linearly-extracted canonical features that are optimal in providing the most important information about the data is determined. An alternative nonlinear feature-selection method is also employed based on spectral indices comprising a small subset of all possible ratios between bands. By applying three optimization strategies, combinations of two and three ratios are found that provide reliable separability and classification between all seven groups according to the Bhattacharyya distance. To set a benchmark to which the MTI capability in rock classification can be compared, an optimization strategy is performed for the selection of optimal multispectral filters, other than the MTI filters, and an improvement in classification is predicted.