{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T16:04:30Z","timestamp":1783785870575,"version":"3.55.0"},"reference-count":64,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T00:00:00Z","timestamp":1690502400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Development Project of Jilin Province","award":["20210203016SF"],"award-info":[{"award-number":["20210203016SF"]}]},{"name":"Science and Technology Development Project of Jilin Province","award":["41702357"],"award-info":[{"award-number":["41702357"]}]},{"name":"Science and Technology Development Project of Jilin Province","award":["52178042"],"award-info":[{"award-number":["52178042"]}]},{"name":"Science and Technology Development Project of Jilin Province","award":["2020-SF-150"],"award-info":[{"award-number":["2020-SF-150"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["20210203016SF"],"award-info":[{"award-number":["20210203016SF"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41702357"],"award-info":[{"award-number":["41702357"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52178042"],"award-info":[{"award-number":["52178042"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2020-SF-150"],"award-info":[{"award-number":["2020-SF-150"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Scientific and Technological Transformative Special Project of Qinghai Province","award":["20210203016SF"],"award-info":[{"award-number":["20210203016SF"]}]},{"name":"Scientific and Technological Transformative Special Project of Qinghai Province","award":["41702357"],"award-info":[{"award-number":["41702357"]}]},{"name":"Scientific and Technological Transformative Special Project of Qinghai Province","award":["52178042"],"award-info":[{"award-number":["52178042"]}]},{"name":"Scientific and Technological Transformative Special Project of Qinghai Province","award":["2020-SF-150"],"award-info":[{"award-number":["2020-SF-150"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Lithology classification is important in mineral resource exploration, engineering geological exploration, and disaster monitoring. Traditional laboratory methods for the qualitative analysis of rocks are limited by sampling conditions and analytical techniques, resulting in high costs, low efficiency, and the inability to quickly obtain large-scale geological information. Hyperspectral remote sensing technology can classify and identify lithology using the spectral characteristics of rock, and is characterized by fast detection, large coverage area, and environmental friendliness, which provide the application potential for lithological mapping at a large regional scale. In this study, ZY1-02D hyperspectral images were used as data sources to construct a new two-layer extreme gradient boosting (XGBoost) lithology classification model based on the XGBoost decision tree and an improved greedy search algorithm. A total of 153 spectral bands of the preprocessed hyperspectral images were input into the first layer of the XGBoost model. Based on the tree traversal structural characteristics of the leaf nodes in the XGBoost model, three built-in XGBoost importance indexes were split and combined. The improved greedy search algorithm was used to extract the spectral band variables, which were imported into the second layer of the XGBoost model, and the bat algorithm was used to optimize the modeling parameters of XGBoost. The extraction model of rock classification information was constructed, and the classification map of regional surface rock types was drawn. Field verification was performed for the two-layer XGBoost rock classification model, and its accuracy and reliability were evaluated based on four indexes, namely, accuracy, precision, recall, and F1 score. The results showed that the two-layer XGBoost model had a good lithological classification effect, robustness, and adaptability to small sample datasets. Compared with the traditional machine learning model, the two-layer XGBoost model shows superior performance. The accuracy, precision, recall, and F1 score of the verification set were 0.8343, 0.8406, 0.8350, and 0.8157, respectively. The variable extraction ability of the constructed two-layer XGBoost model was significantly improved. Compared with traditional feature selection methods, the GREED-GFC method, when applied to the two-layer XGBoost model, contributes to more stable rock classification performance and higher lithology prediction accuracy, and the smallest number of extracted features. The lithological distribution information identified by the model was in good agreement with the lithology information verified in the field.<\/jats:p>","DOI":"10.3390\/rs15153764","type":"journal-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T01:48:50Z","timestamp":1690768130000},"page":"3764","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Lithological Classification by Hyperspectral Images Based on a Two-Layer XGBoost Model, Combined with a Greedy Algorithm"],"prefix":"10.3390","volume":"15","author":[{"given":"Nan","family":"Lin","sequence":"first","affiliation":[{"name":"School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China"},{"name":"Jilin Province Natural Resources Remote Sensing Information Technology Innovation Laboratory, Changchun 130118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawei","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China"},{"name":"Jilin Province Natural Resources Remote Sensing Information Technology Innovation Laboratory, Changchun 130118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ranzhe","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China"},{"name":"Jilin Province Natural Resources Remote Sensing Information Technology Innovation Laboratory, Changchun 130118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Genjun","family":"Li","sequence":"additional","affiliation":[{"name":"Qinghai Geological Survey Institute, Xining 810012, China"},{"name":"Key Laboratory of Geological Processes and Mineral Resources of the Northern Qinghai-Tibet Plateau, Xining 810012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Yang","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"104538","DOI":"10.1016\/j.chemolab.2022.104538","article-title":"Rock lithological instance classification by hyperspectral images using dimensionality reduction and deep learning","volume":"224","author":"Galdames","year":"2022","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Liu, H., Wu, K., Xu, H., and Xu, Y. (2021). Lithology Classification Using TASI Thermal Infrared Hyperspectral Data with Convolutional Neural Networks. Remote Sens., 13.","DOI":"10.3390\/rs13163117"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"17044","DOI":"10.1080\/10106049.2022.2120639","article-title":"Lithology classification in semi-arid areas based on vegetation suppression integrating microwave and optical remote sensing images: Duolun county, Inner Mongolia autonomous region, China","volume":"37","author":"Lu","year":"2022","journal-title":"Geocarto Int."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s12145-022-00932-2","article-title":"Application of improved support vector machine in geochemical lithology identification","volume":"16","author":"Yin","year":"2023","journal-title":"Earth Sci. Inform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.chemolab.2019.04.006","article-title":"Rock lithological classification by hyperspectral, range 3D and color images","volume":"189","author":"Galdames","year":"2019","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_6","first-page":"225","article-title":"Lithology prediction using well logs: A granular computing approach","volume":"17","author":"Hossain","year":"2021","journal-title":"Int. J. Innov. Comput. Inf. Control"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Hossain, T.M., Watada, J., Aziz, I.A., and Hermana, M. (2020). Machine Learning in Electrofacies Classification and Subsurface Lithology Interpretation: A Rough Set Theory Approach. Appl. Sci., 10.","DOI":"10.3390\/app10175940"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, Z., and Tian, S. (2021). Lithological information extraction and classification in hyperspectral remote sensing data using Backpropagation Neural Network. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0254542"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sun, L., Khan, S., and Shabestari, P. (2019). Integrated Hyperspectral and Geochemical Study of Sediment-Hosted Disseminated Gold at the Goldstrike District, Utah. Remote Sens., 11.","DOI":"10.3390\/rs11171987"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Abd El-Wahed, M., Kamh, S., Abu Anbar, M., Zoheir, B., Hamdy, M., Abdeldayem, A., Lebda, E.M., and Attia, M. (2023). Multisensor Satellite Data and Field Studies for Unravelling the Structural Evolution and Gold Metallogeny of the Gerf Ophiolitic Nappe, Eastern Desert, Egypt. Remote Sens., 15.","DOI":"10.3390\/rs15081974"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3156","DOI":"10.3390\/rs5073156","article-title":"Targeting Mineral Resources with Remote Sensing and Field Data in the Xiemisitai Area, West Junggar, Xinjiang, China","volume":"5","author":"Liu","year":"2013","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1109\/TIP.2016.2542360","article-title":"Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation","volume":"25","author":"Dong","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jackisch, R., Madriz, Y., Zimmermann, R., Pirttijarvi, M., Saartenoja, A., Heincke, B.H., Salmirinne, H., Kujasalo, J.-P., Andreani, L., and Gloaguen, R. (2019). Drone-Borne Hyperspectral and Magnetic Data Integration: Otanmaki Fe-Ti-V Deposit in Finland. Remote Sens., 11.","DOI":"10.3390\/rs11182084"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kuras, A., Heincke, B.H., Salehi, S., Mielke, C., Koellner, N., Rogass, C., Altenberger, U., and Burud, I. (2022). Integration of Hyperspectral and Magnetic Data for Geological Characterization of the Niaqornarssuit Ultramafic Complex in West-Greenland. Remote Sens., 14.","DOI":"10.3390\/rs14194877"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Boubanga-Tombet, S., Huot, A., Vitins, I., Heuberger, S., Veuve, C., Eisele, A., Hewson, R., Guyot, E., Marcotte, F., and Chamberland, M. (2018). Thermal Infrared Hyperspectral Imaging for Mineralogy Mapping of a Mine Face. Remote Sens., 10.","DOI":"10.3390\/rs10101518"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, N., Zhao, T., Zhang, H., Chang, J., Tao, J., and Chi, Y. (2023). Lithium-Bearing Pegmatite Identification, Based on Spectral Analysis and Machine Learning: A Case Study of the Dahongliutan Area, NW China. Remote Sens., 15.","DOI":"10.3390\/rs15020493"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e02931","DOI":"10.1016\/j.heliyon.2019.e02931","article-title":"Evaluation of AVIRIS-NG hyperspectral images for mineral identification and mapping","volume":"5","author":"Tripathi","year":"2019","journal-title":"Heliyon"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1007\/s42452-021-04308-x","article-title":"Using geochemical imaging data to map nickel sulfide deposits in Daxinganling, China","volume":"3","author":"Chen","year":"2021","journal-title":"SN Appl. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fonseca, G.S., dos Santos, A.C.G., de Sa, L.B., and Gomes, J.G.R.C. (2021, January 12\u201316). Linear models for SWIR surface spectra from the ECOSTRESS library. Proceedings of the Conference on Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII, Online.","DOI":"10.1117\/12.2587752"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, D., Zhang, L., Sun, X., Gao, Y., Lan, Z., Wang, Y., Zhai, H., Li, J., Wang, W., and Chen, M. (2022). A New Method for Calculating Water Quality Parameters by Integrating Space-Ground Hyperspectral Data and Spectral-In Situ Assay Data. Remote Sens., 14.","DOI":"10.20944\/preprints202205.0387.v1"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.cageo.2015.03.013","article-title":"Predictive lithological mapping of Canada's North using Random Forest classification applied to geophysical and geochemical data","volume":"80","author":"Harris","year":"2015","journal-title":"Comput. Geosci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, H., Cui, J., Zhang, X., Han, Y., and Cao, L. (2022). Dimensionality Reduction and Classification of Hyperspectral Remote Sensing Image Feature Extraction. Remote Sens., 14.","DOI":"10.3390\/rs14184579"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shi, G., Luo, F., Tang, Y., and Li, Y. (2021). Dimensionality Reduction of Hyperspectral Image Based on Local Constrained Manifold Structure Collaborative Preserving Embedding. Remote Sens., 13.","DOI":"10.3390\/rs13071363"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, T., Jin, X., Gu, Y., and IEEE (2016, January 21\u201323). Sparse Multiple Kernel Learning for Hyperspectral Image Classification Using Spatial-spectral Features. Proceedings of the 6th International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC), Harbin, China.","DOI":"10.1109\/IMCCC.2016.180"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Huang, W., Li, W., Xu, J., Ma, X., Li, C., and Liu, C. (2022). Hyperspectral Monitoring Driven by Machine Learning Methods for Grassland Above-Ground Biomass. Remote Sens., 14.","DOI":"10.3390\/rs14092086"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"109330","DOI":"10.1016\/j.ecolind.2022.109330","article-title":"Estimating the heavy metal contents in farmland soil from hyperspectral images based on Stacked AdaBoost ensemble learning","volume":"143","author":"Lin","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xu, Y., Wang, J., Xia, A., Zhang, K., Dong, X., Wu, K., and Wu, G. (2019). Continuous Wavelet Analysis of Leaf Reflectance Improves Classification Accuracy of Mangrove Species. Remote Sens., 11.","DOI":"10.3390\/rs11030254"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Feng, Y., Lv, J., and Su, J. (2009, January 25\u201327). Feature Preserving Compression for Hyperspectral Remote Sensing Images. Proceedings of the 4th IEEE Conference on Industrial Electronics and Applications, Xian, China.","DOI":"10.1109\/ICIEA.2009.5138926"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Banskota, A., Wynne, R.H., Thomas, V.A., Serbin, S.P., Kayastha, N., Gastellu-Etchegorry, J.P., and Townsend, P.A. (2013). Investigating the Utility of Wavelet Transforms for Inverting a 3-D Radiative Transfer Model Using Hyperspectral Data to Retrieve Forest LAI. Remote Sens., 5.","DOI":"10.3390\/rs5062639"},{"key":"ref_30","unstructured":"Yu, Y., Peng, Y., Jiang, T., and Na, J. (2020, January 25\u201327). An endmember extraction method based on PCA and a new SGA algorithm. Proceedings of the Applied Optics and Photonics China (AOPC) Conference\u2014Optical Sensing and Imaging Technology, Xiamen, China."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhou, L., Ma, X., Wang, X., Hao, S., Ye, Y., and Zhao, K. (2023). Shallow-to-Deep Spatial-Spectral Feature Enhancement for Hyperspectral Image Classification. Remote Sens., 15.","DOI":"10.3390\/rs15010261"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2659","DOI":"10.1109\/JSTARS.2014.2312539","article-title":"Optimized Hyperspectral Band Selection Using Particle Swarm Optimization","volume":"7","author":"Su","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, J., Ding, S., and IEEE (2012, January 23\u201325). Spectral Feature Selection with Particle Swarm Optimization for Hyperspectral Classification. Proceedings of the International Conference on Industrial Control and Electronics Engineering (ICICEE), Xian, China.","DOI":"10.1109\/ICICEE.2012.116"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4835932","DOI":"10.1155\/2016\/4835932","article-title":"Improved Ant Colony Clustering Algorithm and Its Performance Study","volume":"2016","author":"Gao","year":"2016","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"102992","DOI":"10.1109\/ACCESS.2022.3199871","article-title":"Feature Selection for Cross-Scene Hyperspectral Image Classification via Improved Ant Colony Optimization Algorithm","volume":"10","author":"Yu","year":"2022","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"100806","DOI":"10.1016\/j.swevo.2020.100806","article-title":"A multi-strategy integrated multi-objective artificial bee colony for unsupervised band selection of hyperspectral images","volume":"60","author":"Zhang","year":"2021","journal-title":"Swarm Evol. Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1952","DOI":"10.1109\/TIP.2023.3258739","article-title":"Multi-Objective Unsupervised Band Selection Method for Hyperspectral Images Classification","volume":"32","author":"Ou","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1109\/TGRS.2017.2744662","article-title":"Random Forest Ensembles and Extended Multiextinction Profiles for Hyperspectral Image Classification","volume":"56","author":"Xia","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, J., Zhang, H., Zhao, J., Guo, X., Rihan, W., and Deng, G. (2022). Embedded Feature Selection and Machine Learning Methods for Flash Flood Susceptibility-Mapping in the Mainstream Songhua River Basin, China. Remote Sens., 14.","DOI":"10.3390\/rs14215523"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Xu, S., Liu, S., Wang, H., Chen, W., Zhang, F., and Xiao, Z. (2021). A Hyperspectral Image Classification Approach Based on Feature Fusion and Multi-Layered Gradient Boosting Decision Trees. Entropy, 23.","DOI":"10.3390\/e23010020"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Peng, S., Xi, X., Wang, C., Dong, P., Wang, P., and Nie, S. (2019). Systematic Comparison of Power Corridor Classification Methods from ALS Point Clouds. Remote Sens., 11.","DOI":"10.3390\/rs11171961"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1007\/s13198-020-01049-9","article-title":"Performance analysis of regression algorithms and feature selection techniques to predict PM2.5 in smart cities","volume":"14","author":"Banga","year":"2023","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/B978-0-12-818597-1.50019-9","article-title":"Gradient boosted decision trees for lithology classification","volume":"47","author":"Dev","year":"2019","journal-title":"Comput. Aided Chem. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1640096","DOI":"10.1155\/2022\/1640096","article-title":"Lithology Logging Recognition Technology Based on GWO-SVM Algorithm","volume":"2022","author":"Lu","year":"2022","journal-title":"Math. Probl. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, H., Wu, Y., Cao, Y., Lv, W., Han, H., Li, Z., and Chang, J. (2020). Well Logging Based Lithology Identification Model Establishment Under Data Drift: A Transfer Learning Method. Sensors, 20.","DOI":"10.3390\/s20133643"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"104443","DOI":"10.1016\/j.jappgeo.2021.104443","article-title":"Volcanic lithology identification based on parameter-optimized GBDT algorithm: A case study in the Jilin Oilfield, Songliao Basin, NE China","volume":"194","author":"Yu","year":"2021","journal-title":"J. Appl. Geophys."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1637","DOI":"10.1007\/s11424-022-1059-y","article-title":"Lithology Classification Based on Set-Valued Identification Method","volume":"35","author":"Li","year":"2022","journal-title":"J. Syst. Sci. Complex."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physa.2017.12.018","article-title":"A link prediction method for heterogeneous networks based on BP neural network","volume":"495","author":"Li","year":"2018","journal-title":"Phys. A-Stat. Mech. Its Appl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1088\/1742-2140\/aa5b5b","article-title":"Support vector machine as an alternative method for lithology classification of crystalline rocks","volume":"14","author":"Deng","year":"2017","journal-title":"J. Geophys. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2734","DOI":"10.1109\/TGRS.2012.2211882","article-title":"Combining Support Vector Machines and Markov Random Fields in an Integrated Framework for Contextual Image Classification","volume":"51","author":"Moser","year":"2013","journal-title":"Ieee Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1007\/s12594-016-0507-5","article-title":"Performance of image classification on hyperspectral imagery for lithological mapping","volume":"88","author":"Rani","year":"2016","journal-title":"J. Geol. Soc. India"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"104747","DOI":"10.1016\/j.jappgeo.2022.104747","article-title":"A variational inequality approach with SVM optimization algorithm for identifying mineral lithology","volume":"204","author":"Mou","year":"2022","journal-title":"J. Appl. Geophys."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"104475","DOI":"10.1016\/j.cageo.2020.104475","article-title":"Evaluation of machine learning methods for lithology classification using geophysical data","volume":"139","author":"Bressan","year":"2020","journal-title":"Comput. Geosci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1007\/s12517-017-3116-8","article-title":"Lithological classification and chemical component estimation based on the visual features of crushed rock samples","volume":"10","author":"Khorram","year":"2017","journal-title":"Arab. J. Geosci."},{"key":"ref_55","unstructured":"Ethem, A. (2014). Introduction to Machine Learning, MIT Press."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1016\/j.jappgeo.2018.09.011","article-title":"Permeability prediction of isolated channel sands using machine learning","volume":"159","author":"Zhang","year":"2018","journal-title":"J. Appl. Geophys."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","article-title":"Deep learning for visual understanding: A review","volume":"187","author":"Guo","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.jappgeo.2018.06.012","article-title":"Machine learning approaches for petrographic classification of carbonate-siliciclastic rocks using well logs and textural information","volume":"155","author":"Saporetti","year":"2018","journal-title":"J. Appl. Geophys."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1038\/nmeth.3707","article-title":"Deep learning","volume":"13","author":"Rusk","year":"2016","journal-title":"Nat. Methods"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"109520","DOI":"10.1016\/j.petrol.2021.109520","article-title":"An optimized XGBoost method for predicting reservoir porosity using petrophysical logs","volume":"208","author":"Pan","year":"2022","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"104798","DOI":"10.1016\/j.jseaes.2021.104798","article-title":"Lithological classification via an improved extreme gradient boosting: A demonstration of the Chang 4+5 member, Ordos Basin, Northern China","volume":"215","author":"Gu","year":"2021","journal-title":"J. Asian Earth Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"104480","DOI":"10.1016\/j.jappgeo.2021.104480","article-title":"Lithology identification of igneous rocks based on XGboost and conventional logging curves, a case study of the eastern depression of Liaohe Basin","volume":"195","author":"Han","year":"2021","journal-title":"J. Appl. Geophys."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"105350","DOI":"10.1016\/j.mtcomm.2023.105350","article-title":"Prediction of CSG splitting tensile strength based on XGBoost-RF model","volume":"34","author":"Guo","year":"2023","journal-title":"Mater. Today Commun."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Chandrahas, N.S., Choudhary, B.S., Teja, M.V., Venkataramayya, M.S., and Prasad, N.S.R.K. (2022). XG Boost Algorithm to Simultaneous Prediction of Rock Fragmentation and Induced Ground Vibration Using Unique Blast Data. Appl. Sci., 12.","DOI":"10.3390\/app12105269"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3764\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:21:55Z","timestamp":1760127715000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3764"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,28]]},"references-count":64,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15153764"],"URL":"https:\/\/doi.org\/10.3390\/rs15153764","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,28]]}}}