{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T06:13:14Z","timestamp":1776406394082,"version":"3.51.2"},"reference-count":79,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T00:00:00Z","timestamp":1629244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"EIT RawMaterials","award":["iTARG3T 18036"],"award-info":[{"award-number":["iTARG3T 18036"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study aims to assess the feasibility of delineating and identifying mineral ores from hyperspectral images of tin\u2013tungsten mine excavation faces using machine learning classification. We compiled a set of hand samples of minerals of interest from a tin\u2013tungsten mine and analyzed two types of hyperspectral images: (1) images acquired with a laboratory set-up under close-to-optimal conditions, and (2) a scan of a simulated mine face using a field set-up, under conditions closer to those in the gallery. We have analyzed the following minerals: cassiterite (tin ore), wolframite (tungsten ore), chalcopyrite, malachite, muscovite, and quartz. Classification (Linear Discriminant Analysis, Singular Vector Machines and Random Forest) of laboratory spectra had a very high overall accuracy rate (98%), slightly lower if the 450\u2013950 nm and 950\u20131650 nm ranges are considered independently, and much lower (74.5%) for simulated conventional RGB imagery. Classification accuracy for the simulation was lower than in the laboratory but still high (85%), likely a consequence of the lower spatial resolution. All three classification methods performed similarly in this case, with Random Forest producing results of slightly higher accuracy. The user\u2019s accuracy for wolframite was 85%, but cassiterite was often confused with wolframite (user\u2019s accuracy: 70%). A lumped ore category achieved 94.9% user\u2019s accuracy. Our study confirms the suitability of hyperspectral imaging to record the spatial distribution of ore mineralization in progressing tungsten\u2013tin mine faces.<\/jats:p>","DOI":"10.3390\/rs13163258","type":"journal-article","created":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T22:51:00Z","timestamp":1629327060000},"page":"3258","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Machine Learning for Mineral Identification and Ore Estimation from Hyperspectral Imagery in Tin\u2013Tungsten Deposits: Simulation under Indoor Conditions"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6689-2908","authenticated-orcid":false,"given":"Agustin","family":"Lobo","sequence":"first","affiliation":[{"name":"Geosciences Barcelona, GEO3BCN-CSIC, 08028 Barcelona, Spain"}]},{"given":"Emma","family":"Garcia","sequence":"additional","affiliation":[{"name":"Lithica (SCCL), 17430 Sta Coloma de Farners, Spain"}]},{"given":"Gisela","family":"Barroso","sequence":"additional","affiliation":[{"name":"Remote Sensing and GIS, Universitat Aut\u00f2noma de Barcelona, 08193 Cerdanyola del Vall\u00e8s, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5502-921X","authenticated-orcid":false,"given":"David","family":"Mart\u00ed","sequence":"additional","affiliation":[{"name":"Lithica (SCCL), 17430 Sta Coloma de Farners, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4383-799X","authenticated-orcid":false,"given":"Jose-Luis","family":"Fernandez-Turiel","sequence":"additional","affiliation":[{"name":"Geosciences Barcelona, GEO3BCN-CSIC, 08028 Barcelona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8909-6541","authenticated-orcid":false,"given":"Jordi","family":"Ib\u00e1\u00f1ez-Insa","sequence":"additional","affiliation":[{"name":"Geosciences Barcelona, GEO3BCN-CSIC, 08028 Barcelona, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1126\/science.228.4704.1147","article-title":"Imaging Spectrometry for Earth Remote Sensing","volume":"228","author":"Goetz","year":"1985","journal-title":"Science"},{"key":"ref_2","unstructured":"Amigo Rubio, J.M. (2020). Hyperspectral Imaging, Elsevier. Data Handling in Science and Technology."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1807","DOI":"10.1080\/01431168708954818","article-title":"Principles of field spectroscopy","volume":"8","author":"Milton","year":"1987","journal-title":"Int. Remote Sens."},{"key":"ref_4","first-page":"3","article-title":"Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy","volume":"Volume 3","author":"Rencz","year":"1999","journal-title":"Remote Sensing for the Earth Sciences: Manual of Remote Sensing"},{"key":"ref_5","first-page":"283","article-title":"Visible and Near-Infrared Spectra of Minerals and Rocks: I Silicate Minerals","volume":"1","author":"Hunt","year":"1970","journal-title":"Mod. Geol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1190\/1.1440721","article-title":"Spectral signatures of particulate minerals in the visible and near infrared","volume":"42","author":"Hunt","year":"1977","journal-title":"Geophysics"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"12653","DOI":"10.1029\/JB095iB08p12653","article-title":"High spectral resolution reflectance spectroscopy of minerals","volume":"95","author":"Clark","year":"1990","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_8","first-page":"112","article-title":"Multi- and hyperspectral geologic remote sensing: A review","volume":"14","author":"Hecker","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","first-page":"031501","article-title":"Hyperspectral Remote Sensing in Lithological Mapping, Mineral Exploration, and Environmental Geology: An Updated Review","volume":"15","author":"Peyghambari","year":"2021","journal-title":"JARS"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bedell, R., Cr\u00f3sta, A.P., and Grunski, E. (2009). Remote Sensing and Spectral Geology, Society of Economic Geologists.","DOI":"10.5382\/Rev.16"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Pour, A.B., Zoheir, B., Pradhan, B., and Hashim, M. (2021). Editorial for the Special Issue: Multispectral and Hyperspectral Remote Sensing Data for Mineral Exploration and Environmental Monitoring of Mined Areas. Remote Sens., 13.","DOI":"10.3390\/rs13030519"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gupta, R.P. (2018). Imaging Spectroscopy. Remote Sensing Geology, Springer.","DOI":"10.1007\/978-3-662-55876-8"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1957","DOI":"10.1007\/s12665-011-1422-0","article-title":"Pyrite mine waste and water mapping using Hymap and Hyperion hyperspectral data","volume":"66","author":"Riaza","year":"2012","journal-title":"Environ. Earth Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Buzzi, J., Riaza, A., Garc\u00eda-Mel\u00e9ndez, E., Weide, S., and Bachmann, M. (2014). Mapping Changes in a Recovering Mine Site with Hyperspectral Airborne HyMap Imagery (Sotiel, SW Spain). Minerals, 4.","DOI":"10.3390\/min4020313"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Song, W., Song, W., Gu, H., and Li, F. (2020). Progress in the Remote Sensing Monitoring of the Ecological Environment in Mining Areas. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17061846"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1190\/1.1620630","article-title":"Sulfide detection in drill core from the Stillwater Complex using visible\/near-infrared imaging spectroscopy","volume":"68","author":"Bolin","year":"2003","journal-title":"Geophysics"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1780","DOI":"10.1080\/01431161.2011.600350","article-title":"Mapping alteration minerals at prospect, outcrop and drill core scales using imaging spectrometry","volume":"33","author":"Kruse","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.gexplo.2016.09.008","article-title":"Alteration mapping on drill cores using a HySpex SWIR-320m hyperspectral camera: Application to the exploration of an unconformity-related uranium deposit (Saskatchewan, Canada)","volume":"172","author":"Mathieu","year":"2017","journal-title":"J. Geochem. Explor."},{"key":"ref_19","unstructured":"Tschirhart, V., and Thomas, M.D. (2017). Advances in Spectral Geology and Remote Sensing: 2008\u20132017. Proceedings of the Exploration 17: Sixth Decennial International Conference on Mineral Exploration, Decennial Mineral Exploration Conferences."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3066","DOI":"10.1109\/TGRS.2011.2178419","article-title":"Evaluating Classification Techniques for Mapping Vertical Geology Using Field-Based Hyperspectral Sensors","volume":"50","author":"Murphy","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Broekmans, M.A.T.M. (2012). Hyperspectral Imaging of Iron Ores. Proceedings of the 10th International Congress for Applied Mineralogy (ICAM), Springer.","DOI":"10.1007\/978-3-642-27682-8"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Okyay, \u00dc., Khan, S., Lakshmikantha, M., and Sarmiento, S. (2016). Ground-Based Hyperspectral Image Analysis of the Lower Mississippian (Osagean) Reeds Spring Formation Rocks in Southwestern Missouri. Remote Sens., 8.","DOI":"10.3390\/rs8121018"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"781","DOI":"10.14358\/PERS.84.12.781","article-title":"Spatial Co-Registration and Spectral Concatenation of Panoramic Ground-Based Hyperspectral Images","volume":"84","author":"Okyay","year":"2018","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102952","DOI":"10.1016\/j.earscirev.2019.102952","article-title":"Close-range, ground-based hyperspectral imaging for mining applications at various scales: Review and case studies","volume":"198","author":"Krupnik","year":"2019","journal-title":"Earth-Sci. Rev."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1180\/0026461046820189","article-title":"Multispectral imaging of ore minerals in optical microscopy","volume":"68","author":"Pirard","year":"2004","journal-title":"Mineral. Mag."},{"key":"ref_26","unstructured":"Pirard, E., Bernhardt, H.-J., Catalina, J.-C., Brea, C., Segundo, F., and Castroviejo, R. (2008, January 8\u201310). From Spectrophotometry to Multispectral Imaging of Ore Minerals in Visible and Near Infrared (VNIR) Microscopy. Proceedings of the 9th International Congress for Applied Mineralogy (ICAM 2008), Brisbane, Australia."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Berrezueta, E., Ord\u00f3\u00f1ez-Casado, B., Bonilla, W., Banda, R., Castroviejo, R., Carri\u00f3n, P., and Puglla, S. (2016). Ore Petrography Using Optical Image Analysis: Application to Zaruma-Portovelo Deposit (Ecuador). Geosciences, 6.","DOI":"10.3390\/geosciences6020030"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"106136","DOI":"10.1016\/j.mineng.2019.106136","article-title":"Automated ore microscopy based on multispectral measurements of specular reflectance. A comparative study of some supervised classification techniques","volume":"146","author":"Catalina","year":"2020","journal-title":"Miner. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"511","DOI":"10.5721\/EuJRS20154829","article-title":"Mapping clay minerals in an open-pit mine using hyperspectral and LiDAR data","volume":"48","author":"Murphy","year":"2015","journal-title":"Eur. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jakob, S., Zimmermann, R., and Gloaguen, R. (2017). The Need for Accurate Geometric and Radiometric Corrections of Drone-Borne Hyperspectral Data for Mineral Exploration: MEPHySTo\u2014A Toolbox for Pre-Processing Drone-Borne Hyperspectral Data. Remote Sens., 9.","DOI":"10.3390\/rs9010088"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lorenz, S., Salehi, S., Kirsch, M., Zimmermann, R., Unger, G., Vest S\u00f8rensen, E., and Gloaguen, R. (2018). Radiometric Correction and 3D Integration of Long-Range Ground-Based Hyperspectral Imagery for Mineral Exploration of Vertical Outcrops. Remote Sens., 10.","DOI":"10.3390\/rs10020176"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The spectral image processing system (SIPS) interactive visualization and analysis of imaging spectrometer data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Varshney, P.K., and Arora, M. (2004). Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data, Springer.","DOI":"10.1007\/978-3-662-05605-9"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"van der Linden, S., Rabe, A., Held, M., Jakimow, B., Leit\u00e3o, P., Okujeni, A., Schwieder, M., Suess, S., and Hostert, P. (2015). The EnMAP-Box\u2014A Toolbox and Application Programming Interface for EnMAP Data Processing. Remote Sens., 7.","DOI":"10.3390\/rs70911249"},{"key":"ref_35","unstructured":"Tschirhart, V., and Thomas, M.D. (2017). Multiscale hyperspectral imaging of the Orange Hill Porphyry Copper Deposit, Alaska, USA, with laboratory-, field-, and aircraft-based imaging spectrometers. Proceedings of Exploration 17: Sixth Decennial International Conference on Mineral Exploration, Decennial Mineral Exploration Conferences."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Camps-Valls, G. (2009, January 1\u20134). Machine learning in remote sensing data processing. Proceedings of the 2009 IEEE International Workshop on Machine Learning for Signal Processing, Grenoble, France.","DOI":"10.1109\/MLSP.2009.5306233"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"S110","DOI":"10.1016\/j.rse.2007.07.028","article-title":"Recent advances in techniques for hyperspectral image processing","volume":"113","author":"Plaza","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.cageo.2013.10.008","article-title":"Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information","volume":"63","author":"Cracknell","year":"2014","journal-title":"Comput. Geosci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Krupnik, D., and Khan, S.D. (2020). High-Resolution Hyperspectral Mineral Mapping: Case Studies in the Edwards Limestone, Texas, USA and Sulfide-Rich Quartz Veins from the Ladakh Batholith, Northern Pakistan. Minerals, 10.","DOI":"10.3390\/min10110967"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Quesada, C., and Oliveira, J.T. (2019). Variscan Cycle. The Geology of Iberia: A Geodynamic Approach: Volume 2: The Variscan Cycle, Springer. Regional Geology Reviews.","DOI":"10.1007\/978-3-030-10519-8"},{"key":"ref_41","first-page":"595","article-title":"Mineralizaciones estannovolfram\u00edferas en Noia y Lousame: Estudio previo","volume":"3","year":"1982","journal-title":"Cadernos Lab. Xeol. Laxe Revista Xeol. Galega Hercinico Penins."},{"key":"ref_42","first-page":"241","article-title":"Estudio de las inclusiones fluidas atrapadas en cristales de casiterita y cuarzo del yacimiento de San Finx (La Coru\u00f1a, Espa\u00f1a)","volume":"12","year":"1989","journal-title":"Bol. Sociedad Esp. Mineral."},{"key":"ref_43","first-page":"1","article-title":"Estudio cristalogr\u00e1fico de la Bertrandita de las minas de San Finx (A Coru\u00f1a, Espa\u00f1a)","volume":"19","year":"2014","journal-title":"Macla"},{"key":"ref_44","unstructured":"Llana-Funez, S. (2001). La Estructura de la Unidad de Malpica-Tui (Cordillera Varisca en Iberia), Universidad de Oviedo."},{"key":"ref_45","unstructured":"R Core Team (2018). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_46","unstructured":"Hijmans, R.J. (2021, April 19). Raster: Geographic Data Analysis and Modeling. Available online: https:\/\/cran.r-project.org\/web\/packages\/raster\/index.html."},{"key":"ref_47","unstructured":"Bivand, R., Keitt, T., and Rowlingson, B. (2021, April 19). Rgdal: Bindings for the \u201cGeospatial\u201d Data Abstraction Library. Available online: https:\/\/cran.r-project.org\/web\/packages\/rgdal\/index.html."},{"key":"ref_48","unstructured":"Leutner, B., Horning, N., and Schwalb-Willmann, J. (2021, April 19). RStoolbox: Tools for Remote Sensing Data Analysis. Available online: https:\/\/cran.r-project.org\/web\/packages\/RStoolbox\/RStoolbox.pdf."},{"key":"ref_49","unstructured":"O\u2019Brien, J. (2021, April 19). GdalUtilities: R Wrappers for the GDAL Utilities Executables Shipped with Sf. Available online: https:\/\/cran.r-project.org\/web\/packages\/gdalUtilities\/index.html."},{"key":"ref_50","unstructured":"Reudenbach, C. (2021, April 19). Link2GI: Linking Geographic Information Systems, Remote Sensing and Other Command Line Tools. Available online: https:\/\/cran.r-project.org\/web\/packages\/link2GI\/link2GI.pdf."},{"key":"ref_51","unstructured":"GDAL\/OGR contributors (2019). GDAL\/OGR Geospatial Data Abstraction Software Library, Open Source Geospatial Foundation."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s40965-017-0031-6","article-title":"Orfeo ToolBox: Open source processing of remote sensing images","volume":"2","author":"Grizonnet","year":"2017","journal-title":"Open Geospat. Data Softw. Stand."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"McInerney, D., and Kempeneers, P. (2015). Pktools. Open Source Geospatial Tools: Applications in Earth Observation, Springer. Earth Systems Data and Models.","DOI":"10.1007\/978-3-319-01824-9"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Lowe, D.G. (1999, January 20\u201327). Object recognition from local scale-invariant features. Proceedings of the Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece.","DOI":"10.1109\/ICCV.1999.790410"},{"key":"ref_55","unstructured":"EnMAP Core Science Team (2021, April 19). EnMAP-Box 3-A QGIS Plugin to Process and Visualize Hyperspectral Remote Sensing Data. Available online: https:\/\/enmap-box.readthedocs.io."},{"key":"ref_56","unstructured":"QGIS.org (2021, April 19). QGIS Geographic Information System. Available online: http:\/\/www.qgis.org."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Kokaly, R.F., Clark, R.N., Swayze, G.A., Livo, K.E., Hoefen, T.M., Pearson, N.C., Wise, R.A., Benzel, W.M., Lowers, H.A., and Driscoll, R.L. (2017). USGS Spectral Library Version 7.","DOI":"10.3133\/ds1035"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1016\/j.rse.2008.11.007","article-title":"The ASTER spectral library version 2.0","volume":"113","author":"Baldridge","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"111196","DOI":"10.1016\/j.rse.2019.05.015","article-title":"The ECOSTRESS spectral library version 1.0","volume":"230","author":"Meerdink","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Richards, J.A., and Jia, X. (2005). Remote Sensing Digital Image Analysis: An Introduction, Springer. [4th ed.].","DOI":"10.1007\/3-540-29711-1"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Jiang, J., Liu, D., Gu, J., and Susstrunk, S. (2013, January 15\u201317). What is the space of spectral sensitivity functions for digital color cameras?. Proceedings of the 2013 IEEE Workshop on Applications of Computer Vision (WACV), Washington, DC, USA.","DOI":"10.1109\/WACV.2013.6475015"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Venables, W.N., and Ripley, B.D. (2002). Modern Applied Statistics with S, Springer. [4th ed.].","DOI":"10.1007\/978-0-387-21706-2"},{"key":"ref_63","unstructured":"Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., and Leisch, F. (2020). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien."},{"key":"ref_64","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"149119","DOI":"10.18637\/jss.v028.i05","article-title":"Building Predictive Models in R Using the caret Package","volume":"28","author":"Kuhn","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_66","unstructured":"Castle, M., and Keller, J. (2021, April 19). Rolling Ball Background Subtraction. Available online: https:\/\/imagej.net\/plugins\/rolling-ball.html."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1038\/nmeth.2019","article-title":"Fiji: An open-source platform for biological-image analysis","volume":"9","author":"Schindelin","year":"2012","journal-title":"Nat. Meth."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R Springer Texts in Statistics, Springer. [1st ed.].","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_69","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2013). Applied Predictive Modeling, Springer.","DOI":"10.1007\/978-1-4614-6849-3"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Maxwell, A.E., and Warner, T.A. (2020). Thematic Classification Accuracy Assessment with Inherently Uncertain Boundaries: An Argument for Center-Weighted Accuracy Assessment Metrics. Remote Sens., 12.","DOI":"10.3390\/rs12121905"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/978-3-540-31865-1_25","article-title":"A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation","volume":"Volume 3408","author":"Losada","year":"2005","journal-title":"Advances in Information Retrieval"},{"key":"ref_74","first-page":"227","article-title":"Mineralogical Face-Mapping Using Hyperspectral Scanning for Mine Mapping and Control","volume":"Volume AusIMM","author":"Fraser","year":"2006","journal-title":"Proceedings of the Australasian Institute of Mining and Metallurgy Publication Series"},{"key":"ref_75","first-page":"799","article-title":"Extending geometallurgy to the mine scale with hyperspectral imaging: A pilot study using drone- and ground-based scanning","volume":"38","author":"Barton","year":"2021","journal-title":"Min. Metall. Explor."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"104252","DOI":"10.1016\/j.oregeorev.2021.104252","article-title":"Multi-scale, multi-sensor data integration for automated 3-D geological mapping","volume":"136","author":"Thiele","year":"2021","journal-title":"Ore Geol. Rev."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"4429","DOI":"10.1080\/01431160601034910","article-title":"Multispectral image segmentation by a multichannel watershed-based approach","volume":"28","author":"Li","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Gao, F., Wang, Q., Dong, J., and Xu, Q. (2018). Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs. Remote Sens., 10.","DOI":"10.3390\/rs10081271"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1109\/LGRS.2010.2046618","article-title":"Spatio-Spectral Remote Sensing Image Classification With Graph Kernels","volume":"7","author":"Shervashidze","year":"2010","journal-title":"IEEE Geosci. Remote Sens. 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