{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T15:39:38Z","timestamp":1774280378730,"version":"3.50.1"},"reference-count":76,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T00:00:00Z","timestamp":1696291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MinMatka project","award":["#211751\/31\/2020"],"award-info":[{"award-number":["#211751\/31\/2020"]}]},{"name":"European Regional Development Fund by a Business Finland","award":["#211751\/31\/2020"],"award-info":[{"award-number":["#211751\/31\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Laboratory-based hyperspectral imaging (HSI) is an optical non-destructive technology used to extract mineralogical information from bedrock drill cores. In the present study, drill core scanning in the long-wave infrared (LWIR; 8000\u201312,000 nm) wavelength region was used to map the dominant minerals in HSI pixels. Machine learning classification algorithms, including random forest (RF) and support vector machine, have previously been applied to the mineral characterization of drill core hyperspectral data. The objectives of this study are to expand semi-automated mineral mapping by investigating the mapping accuracy, generalization potential, and classification ability of cutting-edge methods, such as various ensemble machine learning algorithms and deep learning semantic segmentation. In the present study, the mapping of quartz, talc, chlorite, and mixtures thereof in HSI data was performed using the ENVINet5 algorithm, which is based on the U-net deep learning network and four decision tree ensemble algorithms, including RF, gradient-boosting decision tree (GBDT), light gradient-boosting machine (LightGBM), AdaBoost, and bagging. Prior to training the classification models, endmember selection was employed using the Sequential Maximum Angle Convex Cone endmember extraction method to prepare the samples used in the model training and evaluation of the classification results. The results show that the GBDT and LightGBM classifiers outperformed the other classification models with overall accuracies of 89.43% and 89.22%, respectively. The results of the other classifiers showed overall accuracies of 87.32%, 87.33%, 82.74%, and 78.32% for RF, bagging, ENVINet5, and AdaBoost, respectively. Therefore, the findings of this study confirm that the ensemble machine learning algorithms are efficient tools to analyze drill core HSI data and map dominant minerals. Moreover, the implementation of deep learning methods for mineral mapping from HSI drill core data should be further explored and adjusted.<\/jats:p>","DOI":"10.3390\/rs15194806","type":"journal-article","created":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T07:28:29Z","timestamp":1696318109000},"page":"4806","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Leveraging High-Resolution Long-Wave Infrared Hyperspectral Laboratory Imaging Data for Mineral Identification Using Machine Learning Methods"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0377-6405","authenticated-orcid":false,"given":"Alireza","family":"Hamedianfar","sequence":"first","affiliation":[{"name":"Geological Survey of Finland, Information Solutions Unit, P.O. Box 96, FI-02151 Espoo, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4160-3452","authenticated-orcid":false,"given":"Kati","family":"Laakso","sequence":"additional","affiliation":[{"name":"Geological Survey of Finland, Information Solutions Unit, P.O. Box 96, FI-02151 Espoo, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9117-7690","authenticated-orcid":false,"given":"Maarit","family":"Middleton","sequence":"additional","affiliation":[{"name":"Geological Survey of Finland, Information Solutions Unit, P.O. Box 77, FI-96101 Rovaniemi, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6471-617X","authenticated-orcid":false,"given":"Tuomo","family":"T\u00f6rm\u00e4nen","sequence":"additional","affiliation":[{"name":"Geological Survey of Finland, Mineral Economy Solutions Unit, P.O. Box 77, FI-96101 Rovaniemi, Finland"}]},{"given":"Juha","family":"K\u00f6ykk\u00e4","sequence":"additional","affiliation":[{"name":"Geological Survey of Finland, Information Solutions Unit, P.O. Box 77, FI-96101 Rovaniemi, Finland"}]},{"given":"Johanna","family":"Torppa","sequence":"additional","affiliation":[{"name":"Geological Survey of Finland, Information Solutions Unit, P.O. Box 1237, FI-70211 Kuopio, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Hu, Y., Liu, B., Dai, K., and Zhang, Y. (2023). Development of Automatic Electric Drive Drilling System for Core Drilling. Appl. Sci., 13.","DOI":"10.3390\/app13021059"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4829","DOI":"10.1109\/JSTARS.2019.2924292","article-title":"A Machine Learning Framework for Drill-Core Mineral Mapping Using Hyperspectral and High-Resolution Mineralogical Data Fusion","volume":"12","author":"Acosta","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"De La Rosa, R., Tolosana-Delgado, R., Kirsch, M., and Gloaguen, R. (2022). Automated Multi-Scale and Multivariate Geological Logging from Drill-Core Hyperspectral Data. Remote Sens., 14.","DOI":"10.3390\/rs14112676"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"104514","DOI":"10.1016\/j.oregeorev.2021.104514","article-title":"Mineral Quantification at Deposit Scale Using Drill-Core Hyperspectral Data: A Case Study in the Iberian Pyrite Belt","volume":"139","author":"Khodadadzadeh","year":"2021","journal-title":"Ore Geol. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"106150","DOI":"10.1016\/j.mineng.2019.106150","article-title":"Evaluating the Performance of Hyperspectral Short-Wave Infrared Sensors for the Pre-Sorting of Complex Ores Using Machine Learning Methods","volume":"146","author":"Kern","year":"2020","journal-title":"Miner. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"SP527-2022","DOI":"10.1144\/SP527-2022-2","article-title":"The Application of Hyperspectral Core Imaging for Oil and Gas","volume":"527","author":"Linton","year":"2023","journal-title":"Geol. Soc. Lond. Spec. Publ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1080\/01431169608948728","article-title":"Identification and Mapping of Minerals in Drill Core Using Hyperspectral Image Analysis of Infrared Reflectance Spectra","volume":"17","author":"Kruse","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.1007\/s11053-020-09721-4","article-title":"A Historical Overview of the Past Three Decades of Mineral Exploration Technology","volume":"30","author":"Okada","year":"2021","journal-title":"Nat. Resour. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.isprsjprs.2023.05.032","article-title":"A Survey of Machine Learning and Deep Learning in Remote Sensing of Geological Environment: Challenges, Advances, and Opportunities","volume":"202","author":"Han","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"4149","DOI":"10.3390\/rs6054149","article-title":"Determination of Carbonate Rock Chemistry Using Laboratory-Based Hyperspectral Imagery","volume":"6","author":"Zaini","year":"2014","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Abdolmaleki, M., Consens, M., and Esmaeili, K. (2022). Ore-Waste Discrimination Using Supervised and Unsupervised Classification of Hyperspectral Images. Remote Sens., 14.","DOI":"10.3390\/rs14246386"},{"key":"ref_13","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":"Mining, Metall. Explor."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"189","DOI":"10.29150\/jhrs.v7.4.p189-211","article-title":"The Use of Hyperspectral Remote Sensing for Mineral Exploration: A Review","volume":"7","author":"Bedini","year":"2017","journal-title":"J. Hyperspectral Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Laukamp, C., Rodger, A., LeGras, M., Lampinen, H., Lau, I.C., Pejcic, B., Stromberg, J., Francis, N., and Ramanaidou, E. (2021). Mineral Physicochemistry Underlying Feature-Based Extraction of Mineral Abundance and Composition from Shortwave, Mid and Thermal Infrared Reflectance Spectra. Minerals, 11.","DOI":"10.3390\/min11040347"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral Remote Sensing Data Analysis and Future Challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MGRS.2018.2889610","article-title":"Longwave Infrared Hyperspectral Imaging: Principles, Progress, and Challenges","volume":"7","author":"Manolakis","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_18","unstructured":"Gewali, U.B., Monteiro, S.T., and Saber, E. (2018). Machine Learning Based Hyperspectral Image Analysis: A Survey. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"112750","DOI":"10.1016\/j.rse.2021.112750","article-title":"A Review of Machine Learning in Processing Remote Sensing Data for Mineral Exploration","volume":"268","author":"Shirmard","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_20","first-page":"69","article-title":"A Review on Spectral Processing Methods for Geological Remote Sensing","volume":"47","author":"Asadzadeh","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4138","DOI":"10.1109\/TGRS.2011.2161585","article-title":"Analysis of Imaging Spectrometer Data Using N-Dimensional Geometry and a Mixture-Tuned Matched Filtering Approach","volume":"49","author":"Boardman","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5770","DOI":"10.1016\/j.asoc.2011.02.030","article-title":"Supervised and Unsupervised Landuse Map Generation from Remotely Sensed Images Using Ant Based Systems","volume":"11","author":"Halder","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.H., and Friedman, J.H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep Learning in Neural Networks: An Overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wei, Y., Li, X., Pan, X., and Li, L. (2020). Nondestructive Classification of Soybean Seed Varieties by Hyperspectral Imaging and Ensemble Machine Learning Algorithms. Sensors, 20.","DOI":"10.3390\/s20236980"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Jafarzadeh, H., Mahdianpari, M., Gill, E., Mohammadimanesh, F., and Homayouni, S. (2021). Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation. Remote Sens., 13.","DOI":"10.3390\/rs13214405"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.compchemeng.2019.06.001","article-title":"Formation Lithology Classification Using Scalable Gradient Boosted Decision Trees","volume":"128","author":"Dev","year":"2019","journal-title":"Comput. Chem. Eng."},{"key":"ref_28","unstructured":"Qi, M.L. (2017, January 4\u20139). A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the 2017 Conference on Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.1515\/geo-2022-0436","article-title":"Extraction of Mineralized Indicator Minerals Using Ensemble Learning Model Optimized by SSA Based on Hyperspectral Image","volume":"14","author":"Lin","year":"2022","journal-title":"Open Geosci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"871529","DOI":"10.3389\/feart.2022.871529","article-title":"Identifying and Mapping Alteration Minerals Using HySpex Airborne Hyperspectral Data and Random Forest Algorithm","volume":"10","author":"Wang","year":"2022","journal-title":"Front. Earth Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lobo, A., Garcia, E., Barroso, G., Mart\u00ed, D., Fernandez-Turiel, J.-L., and Ib\u00e1\u00f1ez-Insa, J. (2021). Machine Learning for Mineral Identification and Ore Estimation from Hyperspectral Imagery in Tin\u2013Tungsten Deposits: Simulation under Indoor Conditions. Remote Sens., 13.","DOI":"10.20944\/preprints202106.0220.v1"},{"key":"ref_32","first-page":"239","article-title":"Applying Self-Organizing Maps to Characterize Hyperspectral Drill Core Data from Three Ore Prospects in Northern Finland","volume":"Volume 12268","author":"Laakso","year":"2022","journal-title":"Proceedings of the Earth Resources and Environmental Remote Sensing\/GIS Applications XIII"},{"key":"ref_33","unstructured":"Torppa, J., Chudasama, B., Hautala, S., and Kim, Y. (2023, September 04). GisSOM for Clustering Multivariate Data. Available online: https:\/\/tupa.gtk.fi\/raportti\/arkisto\/52_2021.pdf."},{"key":"ref_34","unstructured":"Torppa, J., and Chudasama, B. (2023, September 04). Gissom Software for Multivariate Clustering of Geoscientific Data. Mineral Prospectivity and Exploration Targeting\u2013MinProXT 2021 Webinar 31. Available online: https:\/\/tupa.gtk.fi\/raportti\/arkisto\/57_2021.pdf#page=32."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"821","DOI":"10.5382\/econgeo.4804","article-title":"Quantitative Mineral Mapping of Drill Core Surfaces II: Long-Wave Infrared Mineral Characterization Using \u039cXRF and Machine Learning","volume":"116","author":"Barker","year":"2021","journal-title":"Econ. Geol."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Contreras Acosta, I.C., Khodadadzadeh, M., and Gloaguen, R. (2021). Resolution Enhancement for Drill-Core Hyperspectral Mineral Mapping. Remote Sens., 13.","DOI":"10.3390\/rs13122296"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep Learning-Based Classification of Hyperspectral Data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.isprsjprs.2019.09.006","article-title":"Deep Learning Classifiers for Hyperspectral Imaging: A Review","volume":"158","author":"Paoletti","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1109\/TGRS.2019.2907932","article-title":"Deep Learning for Hyperspectral Image Classification: An Overview","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bell, A., del-Blanco, C.R., Jaureguizar, F., Jurado, M.J., and Garc\u00eda, N. (2022). Automatic Mineral Recognition in Hyperspectral Images Using a Semantic-Segmentation-Based Deep Neural Network Trained on a Hyperspectral Drill-Core Database. SSRN.","DOI":"10.2139\/ssrn.4090740"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid Scene Parsing Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.lithos.2015.07.011","article-title":"Orogenic Gold: Common or Evolving Fluid and Metal Sources through Time","volume":"233","author":"Goldfarb","year":"2015","journal-title":"Lithos"},{"key":"ref_45","first-page":"137","article-title":"The Alteration and Fluid Inclusion Characteristics of the Hirvilavanmaa Gold Deposit, Central Lapland Greenstone Belt, Finland","volume":"44","author":"Hulkki","year":"2007","journal-title":"Geol. Surv. Finland, Spec. Pap."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/12.543794","article-title":"The Sequential Maximum Angle Convex Cone (SMACC) Endmember Model","volume":"Volume 5425","author":"Gruninger","year":"2004","journal-title":"Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"112396","DOI":"10.1016\/j.rse.2021.112396","article-title":"Assessing the Impact of Illumination on UAV Pushbroom Hyperspectral Imagery Collected under Various Cloud Cover Conditions","volume":"258","author":"Kalacska","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_48","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany. Proceedings, Part III 18."},{"key":"ref_49","first-page":"1","article-title":"A Deep Learning Approach for Building Segmentation in Taiwan Agricultural Area Using High Resolution Satellite Imagery","volume":"27","author":"Liu","year":"2022","journal-title":"J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random Forest in Remote Sensing: A Review of Applications and Future Directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y. (2017, January 4\u20139). Lightgbm: A Highly Efficient Gradient Boosting Decision Tree. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_53","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_54","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic Gradient Boosting","volume":"38","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1111\/j.1365-2656.2008.01390.x","article-title":"A Working Guide to Boosted Regression Trees","volume":"77","author":"Elith","year":"2008","journal-title":"J. Anim. Ecol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"B\u00fchlmann, P. (2012). Bagging, Boosting and Ensemble Methods, Springer.","DOI":"10.1007\/978-3-642-21551-3_33"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"627","DOI":"10.14358\/PERS.70.5.627","article-title":"Thematic Map Comparison: Evaluating the Statistical Significance of Differences in Classification Accuracy","volume":"70","author":"Foody","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2019). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press.","DOI":"10.1201\/9780429052729"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/S0034-4257(98)00010-8","article-title":"Design and Analysis for Thematic Map Accuracy Assessment: Fundamental Principles","volume":"64","author":"Stehman","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_61","unstructured":"Goutte, C., and Gaussier, E. (2005, January 21\u201323). A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation. Proceedings of the Advances in Information Retrieval: 27th European Conference on IR Research, ECIR 2005, Santiago de Compostela, Spain. Proceedings 27."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Salisbury, J.W., Walter, L.S., Vergo, N., and D\u2019Aria, D.M. (2023, September 04). Mid-Infrared (2.1\u201325 Urn) Snectra of Minerals, Available online: https:\/\/pubs.usgs.gov\/of\/1987\/0263\/report.pdf.","DOI":"10.3133\/ofr87263"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/0034-4257(92)90092-X","article-title":"Emissivity of Terrestrial Materials in the 8\u201314 \u039cm Atmospheric Window","volume":"42","author":"Salisbury","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_64","unstructured":"Salisbury, J.W. (1991). Infrared (2.1\u201325 \u03bcm) Spectra of Minerals. Johns Hopkins Univ. Press, 267."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"9192","DOI":"10.1029\/JB094iB07p09192","article-title":"Thermal Infrared (2.5\u201313.5 \u039cm) Spectroscopic Remote Sensing of Igneous Rock Types on Particulate Planetary Surfaces","volume":"94","author":"Salisbury","year":"1989","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Tu\u015fa, L., Khodadadzadeh, M., Contreras, C., Rafiezadeh Shahi, K., Fuchs, M., Gloaguen, R., and Gutzmer, J. (2020). Drill-Core Mineral Abundance Estimation Using Hyperspectral and High-Resolution Mineralogical Data. Remote Sens., 12.","DOI":"10.3390\/rs12071218"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2877","DOI":"10.1080\/01431160500242515","article-title":"Support Vector Machine-based Feature Selection for Land Cover Classification: A Case Study with DAIS Hyperspectral Data","volume":"27","author":"Pal","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_68","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_69","doi-asserted-by":"crossref","unstructured":"Jooshaki, M., Nad, A., and Michaux, S. (2021). A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry. Minerals, 11.","DOI":"10.3390\/min11080816"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Yang, C., Qiu, F., Xiao, F., Chen, S., and Fang, Y. (2023). CBM Gas Content Prediction Model Based on the Ensemble Tree Algorithm with Bayesian Hyper-Parameter Optimization Method: A Case Study of Zhengzhuang Block, Southern Qinshui Basin, North China. Processes, 11.","DOI":"10.3390\/pr11020527"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"101347","DOI":"10.1016\/j.phycom.2021.101347","article-title":"Research on Privacy Protection of Multi Source Data Based on Improved Gbdt Federated Ensemble Method with Different Metrics","volume":"49","author":"Luo","year":"2021","journal-title":"Phys. Commun."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.2355\/isijinternational.ISIJINT-2020-639","article-title":"Ensemble Learning Based Methods for Crown Prediction of Hot-Rolled Strip","volume":"61","author":"Li","year":"2021","journal-title":"ISIJ Int."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Rong, G., Alu, S., Li, K., Su, Y., Zhang, J., Zhang, Y., and Li, T. (2020). Rainfall Induced Landslide Susceptibility Mapping Based on Bayesian Optimized Random Forest and Gradient Boosting Decision Tree Models\u2014A Case Study of Shuicheng County, China. Water, 12.","DOI":"10.3390\/w12113066"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of Machine-Learning Classification in Remote Sensing: An Applied Review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Qin, R., and Liu, T. (2022). A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images\u2014Analysis Unit, Model Scalability and Transferability. Remote Sens., 14.","DOI":"10.3390\/rs14030646"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s10115-015-0870-3","article-title":"Transfer Learning for Class Imbalance Problems with Inadequate Data","volume":"48","author":"Reddy","year":"2016","journal-title":"Knowl. Inf. 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