Automated lithological classification using UAV and machine learning on an open cast mine

References

• Bamford T, Esmaeili K, Schoellig A. 2017. Aerial rock fragmentation analysis in low-light condition using UAV Technology. 38th Application of Computers and Operations Research in the Mineral Industry; Golden, USA.
   Google Scholar
• Beretta F, Peroni R, Costa JFCL. 2017. Stockpile volumetric survey using aircraft imagery in comparison with traditional methods. 38th Application of Computers and Operations Research in the Mineral Industry; Golden, USA.
   Google Scholar
• Beretta F, Shibata H, Cordova R, Peroni R, Azambuja J, Costa JFCL. 2018. Topographic modelling using UAVs compared with traditional survey methods in mining. REM – Int Eng J. 71:463–470. doi: 10.1590/0370-44672017710074
   Google Scholar
• Colomina I, Molina P. 2014. Unmanned aerial system for photogrammetry and remote sensing: a review. ISPRS J Photogramm Remote Sens. 92:79–97. doi: 10.1016/j.isprsjprs.2014.02.013
   Web of Science ®Google Scholar
• Dalm M, Buxton N, Ruitenbeek A. 2017. Discriminating ore and waste in a porphyry copper deposit using short-wavelength infrared (SWIR) hyperspectral imagery. Miner Eng. 105:10–18. doi: 10.1016/j.mineng.2016.12.013
   Web of Science ®Google Scholar
• Dandois J, Olano M, Ellis E. 2015. Optimal altitude, overlap and weather conditions for computer vision UAV estimates of forest structure. Remote Sens (Basel). 7:13895–13920. doi: 10.3390/rs71013895
   Web of Science ®Google Scholar
• Dorn M, Lesjak R, Wiesser M. 2016. Improvement of the standard GNSS/IMU solution for UAVs using already existing redundant navigation sensors. European Navigation Conference (ENC); Helsinki, Finland.
   Google Scholar
• Ehrenfeld A, Egaa A, Guerrero P, Liberman S, Hanna V, Voisin L, Adams M. 2017. Geometallurgical variables characterization using hyperspectral images and machine learning technics. 38th Application of Computers and Operations Research in the Mineral Industry; Golden, USA.
   Google Scholar
• Ferrero A, Forlani G, Roncella R, Voyat H. 2009. Advanced geostructural survey methods applied to rock mass Characterization. Rock Mech Rock Eng. 42:631–665. doi: 10.1007/s00603-008-0010-4
   Web of Science ®Google Scholar
• Ghosh S. 2005. Fundamentals of computational photogrammetry. New Delhi: Concept.
   Google Scholar
• Gigli G, Casagli N. 2011. Semi-automatic extraction of rock mass structural data from high resolution lidar point clouds. Int J Rock Mech Min Sci. 48:187–198. doi: 10.1016/j.ijrmms.2010.11.009
   Web of Science ®Google Scholar
• Hu W, Huang Y, Wei L, Zhang F, Li H. 2015. Deep convolutional neural networks for hyperspectral image classification. J Sens. 2015:1–12. doi: 10.1155/2015/258619
   Web of Science ®Google Scholar
• Hugenholtz CH, Whitehead K, Brown O, Barchyn T, Moorman B, Leclair A, Riddel K, Hamilton T. 2013. Geomorphological mapping with a small unmanned aircraft system (SUAS): feature detection and accuracy assessment of a photogrammetrically-derived digital terrain model. Geomorphology. 194:16–24. doi: 10.1016/j.geomorph.2013.03.023
   Web of Science ®Google Scholar
• Lato M, Voge M. 2012. Automated mapping of rock discontinuities in 3D lidar and photogrammetry models. Int J Rock Mech Min Sci. 54:150–158. doi: 10.1016/j.ijrmms.2012.06.003
   Web of Science ®Google Scholar
• Maerz N, Youssef A, Otoo J, Kassebaum T, Duan Y. 2013. A simple method for measuring discontinuity orientations from terrestrial lidar data. Environ Eng Geosci. 19:185–194. doi: 10.2113/gseegeosci.19.2.185
   Web of Science ®Google Scholar
• Nonami K. 2007. Prospect and recent research development for civil use autonomous unmanned aircraft. J Syst Des Dyn. 1:120–128.
   Google Scholar
• Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, et al. 2011. Scikit-learn: machine learning in Python. J Mach Learn Res. 12:2825–2830.
   Web of Science ®Google Scholar
• Pestov V. 2012. Is the k-NN classifier in high dimensions affected by the curse of dimensionality? Comput Math Appl. 65:1427–1437. doi: 10.1016/j.camwa.2012.09.011
   Web of Science ®Google Scholar
• Riquelme A, Abellán A, Tomás R, Jaboyedoff M. 2014. A new approach for semi-automatic rock mass joints recognition from 3D point clouds. Comput Geosci. 68:38–52. doi: 10.1016/j.cageo.2014.03.014
   Web of Science ®Google Scholar
• Roncella R, Forlani G. 2005. Extraction of planar patches from point clouds to retrieve dip and dip direction of rock discontinuities. Int Arch Photogramm Remote Sens Spatial Inf Sci. XXXVI:162–167.
   Google Scholar
• Walton G, Mils G, Fotopoulos G, Radovanovic R, Stancliffe R. 2016. An approach for automated lithological classification of point clouds. Geosphere. 12:1833–1841. doi: 10.1130/GES01326.1
   Web of Science ®Google Scholar
• Westoby J, Brasington J, Glasser N, Hambrey M, Reynolds J. 2012. Structure from-motion’s photogrammetry: a low-cost, effective tool for geoscience applications. Geomorphology. 179:300–314. doi: 10.1016/j.geomorph.2012.08.021
   Web of Science ®Google Scholar