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Article

Image and Point Data Fusion for Enhanced Discrimination of Ore and Waste in Mining

Resource Engineering Section, Department of Geoscience and Engineering, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands
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Minerals 2020, 10(12), 1110; https://doi.org/10.3390/min10121110
Received: 14 October 2020 / Revised: 3 December 2020 / Accepted: 8 December 2020 / Published: 10 December 2020
Sensor technologies provide relevant information on the key geological attributes in mining. The integration of data from multiple sources is advantageous in making use of the synergy among the outputs for the enhanced characterisation of materials. Sensors produce various types of data. Thus, the fusion of these data requires innovative data-driven strategies. In the present study, the fusion of image and point data is proposed, aiming for the enhanced classification of ore and waste materials in a polymetallic sulphide deposit at 3%, 5% and 7% cut-off grades. The image data were acquired in the visible-near infrared (VNIR) and short-wave infrared (SWIR) regions of the electromagnetic spectrum. The point data cover the mid-wave infrared (MWIR) and long-wave infrared (LWIR) spectral regions. A multi-step methodological approach was developed for the fusion of the image and point data at multiple levels using the supervised and unsupervised classification techniques. Several possible combinations of the data blocks were evaluated to select the optimal combinations in an optimised way. The obtained results indicate that the individual image and point techniques resulted in a successful classification of ore and waste materials. However, the classification performance greatly improved with the fusion of image and point data, where the K-means and support vector classification (SVC) models provided acceptable results. The proposed approach enables a significant reduction in data volume while maintaining the relevant information in the spectra. This is principally beneficial for the integration of data from high-throughput and large data volume sources. Thus, the effectiveness and practicality of the approach can permit the enhanced separation of ore and waste materials in operational mines. View Full-Text
Keywords: image data; point data; data fusion; VNIR; SWIR; MWIR; LWIR; sulphide ore; K-means; SVC image data; point data; data fusion; VNIR; SWIR; MWIR; LWIR; sulphide ore; K-means; SVC
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MDPI and ACS Style

Desta, F.; Buxton, M. Image and Point Data Fusion for Enhanced Discrimination of Ore and Waste in Mining. Minerals 2020, 10, 1110. https://doi.org/10.3390/min10121110

AMA Style

Desta F, Buxton M. Image and Point Data Fusion for Enhanced Discrimination of Ore and Waste in Mining. Minerals. 2020; 10(12):1110. https://doi.org/10.3390/min10121110

Chicago/Turabian Style

Desta, Feven, and Mike Buxton. 2020. "Image and Point Data Fusion for Enhanced Discrimination of Ore and Waste in Mining" Minerals 10, no. 12: 1110. https://doi.org/10.3390/min10121110

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