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 environment with high spatial and spectral resolution. However, autonomous mapping of mineral spectra measured 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 proposed 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 evaluated 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 algorithms, and the consistency of its mapping capability is demonstrated using data acquired at two different times of day.

翻译：矿物遥感测绘及矿石与废石表面的区分是采矿等地质应用中的重要任务。利用地面近距高光谱传感器可远程测量环境反射特性，此类传感器具备高空间与高光谱分辨率，使上述任务成为可能。然而，由于矿物与岩石类别间光谱吸收特征的细微差异以及场景光照的变异性，在露天矿面上实现矿物光谱的自主测绘仍具挑战性。当缺乏标注数据以训练监督学习算法时，问题复杂度进一步加剧。本文提出一种基于无监督方法的矿面光谱测绘流程，该流程融合了高光谱机器学习领域的最新进展。所提流程将无监督算法与自监督算法集成至统一系统中，无需人工标注训练数据即可实现矿面矿物测绘。通过包含矿石赤铁矿与未矿化页岩的露天矿面高光谱图像数据集对该流程进行验证。结果表明，集成系统生成的测绘效果优于其各组成算法，且通过不同时段采集的数据验证了其测绘能力的一致性。