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Unsupervised ore/waste classification on open-cut mine faces using close-range hyperspectral data

Geoscience Frontiers 2023年第4期14卷 89-99页

作者： Lloyd Windrim Arman Melkumyan Richard J.Murphy Anna Chlingaryan Raymond Leung Rio Tinto Centre for mine Automation Australian Centre for Field RoboticsThe University of SydneySydney 2006Australia

The remote mapping of minerals and discrimination of ore and waste on surfaces are important tasks for geological applications such as those in mining.Such tasks have become possible using ground-based,close-range hyperspectral sensors which can remotely measure the reflectance properties of the environ-ment with high spatial and spectral resolution.However,autonomous mapping of mineral spectra mea-sured on an open-cut mine face remains a challenging problem due to the subtleness of differences in spectral absorption features between mineral and rock classes as well as variability in the illumination of the scene.An additional layer of difficulty arises when there is no annotated data available to train a supervised learning algorithm.A pipeline for unsupervised mapping of spectra on a mine face is pro-posed which draws from several recent advances in the hyperspectral machine learning literature.The proposed pipeline brings together unsupervised and self-supervised algorithms in a unified system to map minerals on a mine face without the need for human-annotated training data.The pipeline is eval-uated with a hyperspectral image dataset of an open-cut mine face comprising mineral ore martite and non-mineralised shale.The combined system is shown to produce a superior map to its constituent algo-rithms,and the consistency of its mapping capability is demonstrated using data acquired at two differ-ent times of day.

关键词： Hyperspectral imaging mineral mapping open-cut mine face Machine learning Convolutional neural networks Illumination invariance

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