School of Mechanical and Mining Engineering, The University of Queensland, Brisbane, QLD 4072, Australia.
Plotlogic Pty Ltd., 12 Thompson St., Bowen Hills, QLD 4006, Australia.
Sensors (Basel). 2022 Mar 31;22(7):2687. doi: 10.3390/s22072687.
This work determines whether hyperspectral imaging is suitable for discriminating ore from waste at the point of excavation. A prototype scanning system was developed for this study. This system combined hyperspectral cameras and a three-dimensional LiDAR, mounted on a pan-tilt head, and a positioning system which determined the spatial location of the resultant hyperspectral data cube. This system was used to obtain scans both in the laboratory and at a gold mine in Western Australia. Samples from this mine site were assayed to determine their gold concentration and were scanned using the hyperspectral apparatus in the laboratory to create a library of labelled reference spectra. This library was used as (i) the reference set for spectral angle mapper classification and (ii) a training set for a convolutional neural network classifier. Both classification approaches were found to classify ore and waste on the scanned face with good accuracy when compared to the mine geological model. Greater resolution on the classification of ore grade quality was compromised by the quality and quantity of training data. The work provides evidence that an excavator-mounted hyperspectral system could be used to guide a human or autonomous excavator operator to selectively dig ore and minimise dilution.
这项工作旨在确定高光谱成像技术是否适合在挖掘现场对矿石和废石进行区分。为此研究开发了一个原型扫描系统。该系统将高光谱摄像机和三维激光雷达、安装在转台头上的定位系统结合在一起，该定位系统确定了所得高光谱数据立方体的空间位置。该系统用于在实验室和西澳大利亚的一个金矿进行扫描。从该矿点采集的样品进行了化验，以确定其金浓度，并在实验室使用高光谱仪器进行扫描，以创建标记参考光谱库。该库被用作 (i) 光谱角制图分类的参考集，以及 (ii) 卷积神经网络分类器的训练集。与矿山地质模型相比，这两种分类方法都被发现可以对扫描表面的矿石和废石进行准确分类。但是，由于训练数据的质量和数量，对矿石品位质量的分类分辨率更高。该工作提供了证据表明，安装在挖掘机上的高光谱系统可用于指导人工或自主挖掘机操作员有选择性地挖掘矿石并最大限度地减少稀释。
