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In this study, we compare three commonly used methods for hyperspectral image classification, namely Support Vector Machines (SVMs), Gaussian Processes (GPs) and the Spectral Angle Mapper (SAM). We assess their performance in combination with different kernels (i.e. which use distance-based and angle-based metrics). The assessment is done in two experiments, under ideal conditions in the laboratory...
In this paper we use a machine learning algorithm based on Gaussian Processes (GPs) and the Observation Angle Dependent (OAD) covariance function to classify hyper spectral imagery for the first time. This paper demonstrates the potential of the GP-OAD method for use in autonomous mining to identify and map geology and mineralogy on a vertical mine face. We discuss the importance of independent training...
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