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This paper investigates the performance of machine learning methods for classifying rock types from hyperspectral data. The main objective is to test the impact on classification error rate of calibrating the model's output into class probability estimates. The base classifiers included in this study are: boosted decision trees, support vector machines and logistic regression. The standard algorithm...
This paper presents a ground based system for mapping the geology and the geometry of the environment remotely. The main objective of this work is to develop a framework for a mobile robotic platform that can build 3D geological maps. We investigate classification and registration algorithms that can work without any manual intervention. The system capabilities are demonstrated with data acquired...
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