{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T08:20:24Z","timestamp":1776154824537,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,6]],"date-time":"2021-08-06T00:00:00Z","timestamp":1628208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Defense Pre-Research Foundation of China during the 13th Five-Year Plan Period: the High Spectral Resolution Infrared Space-Based Camera and the Applied Technology","award":["D040104"],"award-info":[{"award-number":["D040104"]}]},{"name":"Military and Civilian Integration for Marine Comprehensive Survey and Application of the Maritime Silk Road","award":["2019061160"],"award-info":[{"award-number":["2019061160"]}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071439"],"award-info":[{"award-number":["62071439"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent decades, lithological mapping techniques using hyperspectral remotely sensed imagery have developed rapidly. The processing chains using visible-near infrared (VNIR) and shortwave infrared (SWIR) hyperspectral data are proven to be available in practice. The thermal infrared (TIR) portion of the electromagnetic spectrum has considerable potential for mineral and lithology mapping. In particular, the abovementioned rocks at wavelengths of 8\u201312 \u03bcm were found to be discriminative, which can be seen as a characteristic to apply to lithology classification. Moreover, it was found that most of the lithology mapping and classification for hyperspectral thermal infrared data are still carried out by traditional spectral matching methods, which are not very reliable due to the complex diversity of geological lithology. In recent years, deep learning has made great achievements in hyperspectral imagery classification feature extraction. It usually captures abstract features through a multilayer network, especially convolutional neural networks (CNNs), which have received more attention due to their unique advantages. Hence, in this paper, lithology classification with CNNs was tested on thermal infrared hyperspectral data using a Thermal Airborne Spectrographic Imager (TASI) at three small sites in Liuyuan, Gansu Province, China. Three different CNN algorithms, including one-dimensional CNN (1-D CNN), two-dimensional CNN (2-D CNN) and three-dimensional CNN (3-D CNN), were implemented and compared to the six relevant state-of-the-art methods. At the three sites, the maximum overall accuracy (OA) based on CNNs was 94.70%, 96.47% and 98.56%, representing improvements of 22.58%, 25.93% and 16.88% over the worst OA. Meanwhile, the average accuracy of all classes (AA) and kappa coefficient (kappa) value were consistent with the OA, which confirmed that the focal method effectively improved accuracy and outperformed other methods.<\/jats:p>","DOI":"10.3390\/rs13163117","type":"journal-article","created":{"date-parts":[[2021,8,8]],"date-time":"2021-08-08T21:35:40Z","timestamp":1628458540000},"page":"3117","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Lithology Classification Using TASI Thermal Infrared Hyperspectral Data with Convolutional Neural Networks"],"prefix":"10.3390","volume":"13","author":[{"given":"Huize","family":"Liu","sequence":"first","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Ke","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Honggen","family":"Xu","sequence":"additional","affiliation":[{"name":"Changsha Center of Natural Resources Comprehensive Survey, China Geological Survey, Changsha 410699, China"}]},{"given":"Ying","family":"Xu","sequence":"additional","affiliation":[{"name":"National Satellite Ocean Application Service, Ministry of Natural Resources, Beijing 100081, China"},{"name":"Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,6]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"2215","DOI":"10.1080\/01431169608948770","article-title":"Review Article Hyperspectral Geological Remote Sensing: Evaluation of Analytical Techniques","volume":"17","author":"Cloutis","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.rse.2007.03.015","article-title":"Integrating Visible, Near-Infrared and Short-Wave Infrared Hyperspectral and Multispectral Thermal Imagery for Geological Mapping at Cuprite, Nevada","volume":"110","author":"Chen","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.gexplo.2016.07.002","article-title":"Hyperspectral Remote Sensing Applied to Uranium Exploration: A Case Study at the Mary Kathleen Metamorphic-Hydrothermal U-REE Deposit, NW, Queensland, Australia","volume":"179","author":"Salles","year":"2017","journal-title":"J. Geochem. Explor."},{"key":"ref_5","unstructured":"Zhang, T.-T., and Liu, F. (2012, January 29\u201331). Application of Hyperspectral Remote Sensing in Mineral Identification and Mapping. Proceedings of the 2012 2nd International Conference on Computer Science and Network Technology (ICCSNT 2012), Changchun, China."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3155","DOI":"10.1109\/JSTARS.2020.2999057","article-title":"Mineral Identification and Mapping by Synthesis of Hyperspectral VNIR\/SWIR and Multispectral TIR Remotely Sensed Data with Different Classifiers","volume":"13","author":"Ni","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.oregeorev.2018.03.012","article-title":"Thermal Infrared Multispectral Remote Sensing of Lithology and Mineralogy Based on Spectral Properties of Materials","volume":"108","author":"Ninomiya","year":"2019","journal-title":"Ore Geol. Rev."},{"key":"ref_8","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."},{"key":"ref_9","first-page":"19","article-title":"Temperature and Emissivity Separation and Mineral Mapping Based On Airborne TASI Hyperspectral Thermal Infrared Data","volume":"40","author":"Cui","year":"2015","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.rse.2016.01.022","article-title":"Automated Lithological Mapping Using Airborne Hyperspectral Thermal Infrared Data: A Case Study from Anchorage Island, Antarctica","volume":"176","author":"Black","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2005.06.009","article-title":"Detecting Lithology with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Multispectral Thermal Infrared \u201cRadiance-at-Sensor\u201d Data","volume":"99","author":"Ninomiya","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.jvolgeores.2013.08.007","article-title":"Exploration of Geothermal Systems Using Hyperspectral Thermal Infrared Remote Sensing","volume":"265","author":"Reath","year":"2013","journal-title":"J. Volcanol. Geothem. Res."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kurata, K., and Yamaguchi, Y. (2019). Integration and Visualization of Mineralogical and Topographical Information Derived from ASTER and DEM Data. Remote Sens., 11.","DOI":"10.3390\/rs11020162"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zhang, L., Zhao, X., Yang, H., Yang, K., Zhang, X., Wang, S., and Sun, H. (2016, January 10\u201315). A New Method of Mineral Absorption Feature Extraction from Vegetation Covered Area. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730416"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s12517-020-5148-8","article-title":"Comparative Analysis of Mineral Mapping for Hyperspectral and Multispectral Imagery","volume":"13","author":"Vignesh","year":"2020","journal-title":"Arab. J. Geosci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ni, L., and Wub, H. (August, January 28). Mineral Identification and Classification by Combining Use of Hyperspectral VNIR\/SWIR and Multispectral TIR Remotely Sensed Data. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898212"},{"key":"ref_18","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_19","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_20","doi-asserted-by":"crossref","unstructured":"Villa, P., Pepe, M., Boschetti, M., and de Paulis, R. (2011, January 24\u201329). Spectral Mapping Capabilities of Sedimentary Rocks Using Hyperspectral Data in Sicily, Italy. Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada.","DOI":"10.1109\/IGARSS.2011.6049741"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/MGRS.2019.2899193","article-title":"Spectral Absorption Feature Analysis for Finding Ore: A Tutorial on Using the Method in Geological Remote Sensing","volume":"7","author":"Hecker","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kopa\u010dkov\u00e1, V., and Kouck\u00e1, L. (2017). Integration of Absorption Feature Information from Visible to Longwave Infrared Spectral Ranges for Mineral Mapping. Remote Sens., 9.","DOI":"10.3390\/rs9101006"},{"key":"ref_23","first-page":"102006","article-title":"Automated Lithological Mapping by Integrating Spectral Enhancement Techniques and Machine Learning Algorithms Using AVIRIS-NG Hyperspectral Data in Gold-Bearing Granite-Greenstone Rocks in Hutti, India","volume":"86","author":"Kumar","year":"2020","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wan, S., Lei, T.C., Ma, H.L., and Cheng, R.W. (2019). The Analysis on Similarity of Spectrum Analysis of Landslide and Bareland through Hyper-Spectrum Image Bands. Water, 11.","DOI":"10.3390\/w11112414"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6867","DOI":"10.3390\/rs6086867","article-title":"Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq)","volume":"6","author":"Othman","year":"2014","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Okada, N., Maekawa, Y., Owada, N., Haga, K., Shibayama, A., and Kawamura, Y. (2020). Automated Identification of Mineral Types and Grain Size Using Hyperspectral Imaging and Deep Learning for Mineral Processing. Minerals, 10.","DOI":"10.3390\/min10090809"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, H., and Shen, Q. (2017). Spectral\u2013Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network. Remote Sens., 9.","DOI":"10.3390\/rs9010067"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wei, F., Feng, F., and Wang, C. (2020). Spatial\u2013Spectral Feature Refinement for Hyperspectral Image Classification Based on Attention-Dense 3D-2D-CNN. Sensors, 20.","DOI":"10.3390\/s20185191"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A Fast Learning Algorithm for Deep Belief Nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1007\/s12517-020-05487-4","article-title":"Classification of Hyperspectral Images by Deep Learning of Spectral-Spatial Features","volume":"13","author":"Ding","year":"2020","journal-title":"Arab. J. Geosci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/LGRS.2018.2889307","article-title":"Landslide Inventory Mapping from Bitemporal Images Using Deep Convolutional Neural Networks","volume":"16","author":"Lei","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.earscirev.2019.02.023","article-title":"Deep Learning and Its Application in Geochemical Mapping","volume":"192","author":"Zuo","year":"2019","journal-title":"Earth Sci. Rev."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep Feature Extraction and Classification of Hyperspectral Images Based On Convolutional Neural Networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2485","DOI":"10.1109\/JSTARS.2020.2983224","article-title":"A Simplified 2D-3D CNN Architecture for Hyperspectral Image Classification Based on Spatial\u2013Spectral Fusion","volume":"13","author":"Yu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","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_36","first-page":"127","article-title":"Regional-scale Mineral Mapping Using ASTER VNIR\/SWIR Data and Validation of Reflectance and Mineral Map Products Using Airborne Hyperspectral CASI\/SASI Data","volume":"33","author":"Jing","year":"2014","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1109\/36.700995","article-title":"A Temperature and Emissivity Separation Algorithm for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Images","volume":"36","author":"Gillespie","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/0034-4257(92)90094-Z","article-title":"Spectral Ratio Method for Measuring Emissivity","volume":"42","author":"Watson","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhao, H., Deng, K., Li, N., Wang, Z., and Wei, W. (2020). Hierarchical Spatial-Spectral Feature Extraction with Long Short Term Memory (LSTM) for Mineral Identification Using Hyperspectral Imagery. Sensors, 20.","DOI":"10.3390\/s20236854"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.jseaes.2017.05.005","article-title":"Integration of Spectral, Spatial and Morphometric Data into Lithological Mapping: A comparison of Different Machine Learning Algorithms in the Kurdistan Region, NE Iraq","volume":"146","author":"Othman","year":"2017","journal-title":"J. Asian Earth Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1109\/JSTARS.2020.3005403","article-title":"Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities","volume":"13","author":"Cheng","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M. (2015, January 11\u201318). Learning Spatiotemporal Features with 3D Convolutional Networks. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.510"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Dong, Y., Yang, C., and Zhang, Y. (2021). Deep Metric Learning with Online Hard Mining for Hyperspectral Classification. Remote Sens., 13.","DOI":"10.3390\/rs13071368"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/LGRS.2019.2918719","article-title":"HybridSN: Exploring 3-D\u20132-D CNN Feature Hierarchy for Hyperspectral Image Classification","volume":"17","author":"Roy","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2811","DOI":"10.1109\/TGRS.2017.2783902","article-title":"When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs","volume":"56","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"258619","DOI":"10.1155\/2015\/258619","article-title":"Deep Convolutional Neural Networks for Hyperspectral Image Classification","volume":"2015","author":"Hu","year":"2015","journal-title":"J. Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3117\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:41:57Z","timestamp":1760164917000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/16\/3117"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,6]]},"references-count":46,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13163117"],"URL":"https:\/\/doi.org\/10.3390\/rs13163117","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,6]]}}}