{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T23:25:30Z","timestamp":1774049130912,"version":"3.50.1"},"reference-count":84,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,30]],"date-time":"2024-03-30T00:00:00Z","timestamp":1711756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The US Department of Energy","award":["DE-EE0008760"],"award-info":[{"award-number":["DE-EE0008760"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mineral mapping from satellite images provides valuable insights into subsurface mineral alteration for geothermal exploration. In previous studies, eight fundamental algorithms were used for mineral mapping utilizing USGS spectra, a collection of reflectance spectra containing samples of minerals, rocks, and soils created by the USGS. We used an ASD FieldSpec 4 Hi-RES NG portable spectrometer to collect spectra for analyzing ASTER images of the Coso Geothermal Field. Then, we established the ground-truth information and the spectral library by analyzing 97 samples. Samples collected from the field were analyzed using the CSIRO TSG (The Spectral Geologist of the Commonwealth Scientific and Industrial Research Organization). Based on the mineralogy study, multiple high-purity spectra of geothermal alteration minerals were selected from collected data, including alunite, chalcedony, hematite, kaolinite, and opal. Eight mineral spectral target detection algorithms were applied to the preprocessed satellite data with a proposed local spectral library. We measured the highest overall accuracy of 87% for alunite, 95% for opal, 83% for chalcedony, 60% for hematite, and 96% for kaolinite out of these eight algorithms. Three, four, five, and eight algorithms were fused to extract mineral alteration with the obtained target detection results. The results prove that the fusion of algorithms gives better results than using individual ones. In conclusion, this paper discusses the significance of evaluating different mapping algorithms. It proposes a robust fusion approach to extract mineral maps as an indicator for geothermal exploration.<\/jats:p>","DOI":"10.3390\/rs16071223","type":"journal-article","created":{"date-parts":[[2024,3,31]],"date-time":"2024-03-31T13:28:00Z","timestamp":1711891680000},"page":"1223","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Mapping Geothermal Indicator Minerals Using Fusion of Target Detection Algorithms"],"prefix":"10.3390","volume":"16","author":[{"given":"Mahmut","family":"Cavur","sequence":"first","affiliation":[{"name":"Mining Engineering Department, Colorado School of Mines, Golden, CO 80401, USA"},{"name":"Management Information System Department, Kadir Has University, \u0130stanbul 34083, T\u00fcrkiye"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9363-4817","authenticated-orcid":false,"given":"Yu-Ting","family":"Yu","sequence":"additional","affiliation":[{"name":"Mining Engineering Department, Colorado School of Mines, Golden, CO 80401, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1680-3719","authenticated-orcid":false,"given":"Ebubekir","family":"Demir","sequence":"additional","affiliation":[{"name":"Mining Engineering Department, Colorado School of Mines, Golden, CO 80401, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7013-9241","authenticated-orcid":false,"given":"Sebnem","family":"Duzgun","sequence":"additional","affiliation":[{"name":"Mining Engineering Department, Colorado School of Mines, Golden, CO 80401, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,30]]},"reference":[{"key":"ref_1","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, U.S. Geological Survey.","DOI":"10.3133\/ds1035"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1089\/153110702762027916","article-title":"Application of Hyperspectral Infrared Analysis of Hydrothermal Alteration on Earth and Mars","volume":"2","author":"Thomas","year":"2002","journal-title":"Astrobiology"},{"key":"ref_3","unstructured":"Thompson, A., Scott, K., Huntington, J., and Yang, K. (2009). Remote Sensing and Spectral Geology, Society of Economic Geologists."},{"key":"ref_4","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_5","first-page":"100980","article-title":"Litho-Structural and Hydrothermal Alteration Mapping for Mineral Prospection in the Maider Basin of Morocco Based on Remote Sensing and Field Investigations","volume":"31","author":"Said","year":"2023","journal-title":"Remote Sens. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Be\u0107, K.B., Grabska, J., and Huck, C.W. (2020). Near-Infrared Spectroscopy in Bio-Applications. Molecules, 25.","DOI":"10.3390\/molecules25122948"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103187","DOI":"10.1016\/j.earscirev.2020.103187","article-title":"Monitoring Inland Water Quality Using Remote Sensing: Potential and Limitations of Spectral Indices, Bio-Optical Simulations, Machine Learning, and Cloud Computing","volume":"205","author":"Sagan","year":"2020","journal-title":"Earth Sci. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"112349","DOI":"10.1016\/j.rse.2021.112349","article-title":"NASA\u2019s Surface Biology and Geology Designated Observable: A Perspective on Surface Imaging Algorithms","volume":"257","author":"Townsend","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.rse.2004.02.013","article-title":"Spectrometry for Urban Area Remote Sensing\u2013Development and Analysis of a Spectral Library from 350 to 2400 Nm","volume":"91","author":"Herold","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_10","unstructured":"Fauvel, M., and Spectral and Spatial Methods for the Classification of Urban Remote Sensing Data Spectral and Spatial Methods for the Classification of Urban Remote Sensing Data (2024, March 26). Signal Image Process. Available online: https:\/\/mistis.inrialpes.fr\/people\/fauvel\/Site\/Publication_files\/plan_these.pdf."},{"key":"ref_11","first-page":"1557","article-title":"Hyperspectral Remote Sensing of Urban Areas: An Overview of Techniques and Applications","volume":"4","author":"Shafri","year":"2012","journal-title":"Res. J. Appl. Sci. Eng. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.5382\/econgeo.2018.4604","article-title":"Mineral and Lithologic Mapping Capability of Worldview 3 Data at Mountain Pass, California, Using True- and False-Color Composite Images, Band Ratios, and Logical Operator Algorithms","volume":"113","author":"Mars","year":"2018","journal-title":"Econ. Geol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cardoso-Fernandes, J., Teodoro, A.C., Lima, A., Perrotta, M., and Roda-Robles, E. (2020). Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives. Appl. Sci., 10.","DOI":"10.3390\/app10051785"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sekandari, M., Masoumi, I., Pour, A.B., Muslim, A.M., Rahmani, O., Hashim, M., Zoheir, B., Pradhan, B., Misra, A., and Aminpour, S.M. (2020). Application of Landsat-8, Sentinel-2, ASTER and Worldview-3 Spectral Imagery for Exploration of Carbonate-Hosted Pb-Zn Deposits in the Central Iranian Terrane (CIT). Remote Sens., 12.","DOI":"10.3390\/rs12081239"},{"key":"ref_15","unstructured":"Avcio\u011flu, E. (2010). Hydrocarbon Microseepage Mapping via Remote Sensing for Gemrik Anticline, Bozova Oil Field, Ad\u0131yaman, Turkey, Middle East Technical University."},{"key":"ref_16","first-page":"209","article-title":"Identification of Hydrocarbon Microseepage Induced Alterations with Spectral Target Detection and Unmixing Algorithms","volume":"74","author":"Soydan","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.geothermics.2014.09.002","article-title":"Remote Sensing of Geothermal-Related Minerals for Resource Exploration in Nevada","volume":"53","author":"Calvin","year":"2015","journal-title":"Geothermics"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1016\/j.rse.2010.05.006","article-title":"Mineral Mapping in the Pyramid Lake Basin: Hydrothermal Alteration, Chemical Precipitates and Geothermal Energy Potential","volume":"114","author":"Kratt","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"102195","DOI":"10.1016\/j.geothermics.2021.102195","article-title":"Lithological Mapping of Waiotapu Geothermal Field (New Zealand) Using Hyperspectral and Thermal Remote Sensing and Ground Exploration Techniques","volume":"96","author":"Kereszturi","year":"2021","journal-title":"Geothermics"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102178","DOI":"10.1016\/j.geothermics.2021.102178","article-title":"VNIR-SWIR Infrared (Imaging) Spectroscopy for Geothermal Exploration: Current Status and Future Directions","volume":"96","author":"Savitri","year":"2021","journal-title":"Geothermics"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.geothermics.2018.01.006","article-title":"Short-Wave Infrared (SWIR) Reflectance Spectrometric Characterisation of Clays from Geothermal Systems of the Taup\u014d Volcanic Zone, New Zealand","volume":"73","author":"Simpson","year":"2018","journal-title":"Geothermics"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.renene.2022.04.113","article-title":"The Geothermal Artificial Intelligence for Geothermal Exploration","volume":"192","author":"Moraga","year":"2022","journal-title":"Renew. Energy"},{"key":"ref_23","first-page":"100240","article-title":"Multispectral Mapping of Evaporite Minerals Using ASTER Data: A Methodological Comparison from Central Turkey","volume":"15","year":"2019","journal-title":"Remote Sens. Appl."},{"key":"ref_24","first-page":"100640","article-title":"Bentonite Clay Minerals Mapping Using ASTER and Field Mineralogical Data: A Case Study from the Eastern Rif Belt, Morocco","volume":"24","author":"Lamrani","year":"2021","journal-title":"Remote Sens. Appl."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s Optical High-Resolution Mission for GMES Operational Services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and Product Vision for Terrestrial Global Change Research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cavur, M., Moraga, J., Sebnem Duzgun, H., Soydan, H., and Jin, G. (2021). Displacement Analysis of Geothermal Field Based on Psinsar and Som Clustering Algorithms: A Case Study of Brady Field, Nevada\u2014USA. Remote Sens., 13.","DOI":"10.3390\/rs13030349"},{"key":"ref_29","unstructured":"Sabin, A., Blake, K., Lazaro, M., Meade, D., Blankenship, D., Kennedy, M., Mcculloch, J., Deoreo, S., Hickman, S., and Glen, J. (2016, January 22\u201324). Geologic Setting of the West Flank, A Forge Site Adjacent to the Coso Geothermal Field. Proceedings of the 41st Workshop on Geothermal Reservoir Engineering, Stanford, CA, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"103332","DOI":"10.1016\/j.oregeorev.2020.103332","article-title":"Recent Advances in the Use of Public Domain Satellite Imagery for Mineral Exploration: A Review of Landsat-8 and Sentinel-2 Applications","volume":"117","author":"Adiri","year":"2020","journal-title":"Ore Geol. Rev."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.oregeorev.2019.02.029","article-title":"Prospectivity Mapping for High Sulfidation Epithermal Porphyry Deposits Using an Integrated Compositional and Topographic Remote Sensing Dataset","volume":"107","author":"Ferrier","year":"2019","journal-title":"Ore Geol. Rev."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"115089","DOI":"10.1016\/j.geoderma.2021.115089","article-title":"Soil Property Maps with Satellite Images at Multiple Scales and Its Impact on Management and Classification","volume":"397","author":"Silvero","year":"2021","journal-title":"Geoderma"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.rser.2014.12.026","article-title":"A Review of Geophysical Methods for Geothermal Exploration","volume":"44","author":"Djongyang","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_34","first-page":"1","article-title":"Using of Remote Sensing and Aeromagnetic Data for Predicting Potential Areas of Hydrothermal Mineral Deposits in the Central Eastern Desert of Egypt","volume":"7","author":"Abdelkareem","year":"2018","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4489","DOI":"10.1166\/asl.2018.11632","article-title":"Using ASTER Satellite Data for Mapping Hydrothermal Alteration as a Tool in Geothermal Exploration with GPS Field Validation","volume":"24","author":"Abubakar","year":"2018","journal-title":"Adv. Sci. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Park, H., and Choi, J. (2021). Mineral Detection Using Sharpened Vnir and Swir Bands of Worldview-3 Satellite Imagery. Sustainability, 13.","DOI":"10.3390\/su13105518"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Flores, H., Lorenz, S., Jackisch, R., Tusa, L., Cecilia Contreras, I., Zimmermann, R., and Gloaguen, R. (2021). UAS-Based Hyperspectral Environmental Monitoring of Acid Mine Drainage Affected Waters. Minerals, 11.","DOI":"10.3390\/min11020182"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Abedini, M., Ziaii, M., Timkin, T., and Pour, A.B. (2023). Machine Learning (ML)-Based Copper Mineralization Prospectivity Mapping (MPM) Using Mining Geochemistry Method and Remote Sensing Satellite Data. Remote Sens., 15.","DOI":"10.3390\/rs15153708"},{"key":"ref_39","unstructured":"Wolfe, J.D., and Black, S.R. (2024, March 26). Hyperspectral Analytics in ENVI; September 19, 2018 Edition. Available online: https:\/\/www.nv5geospatialsoftware.com\/Portals\/0\/pdfs\/Confirmation\/L3HG_Hyperspectral_Whitepaper.pdf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1109\/TSP.2004.840823","article-title":"The Adaptive Coherence Estimator: A Uniformly Most-Powerful-Invariant Adaptive Detection Statistic","volume":"53","author":"Kraut","year":"2005","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_41","first-page":"79","article-title":"Hyperspectral Image Processing for Automatic Target Detection Applications","volume":"14","author":"Manolakis","year":"2003","journal-title":"Linc. Lab. J."},{"key":"ref_42","unstructured":"Harsanyi, J.C., and Chang, C.-I. (1993). Detection and Classification of Subpixel Spectral Signatures in Hyperspectral Image Sequences, University of Maryland."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1117\/1.602486","article-title":"Generalized Constrained Energy Minimization Approach to Subpixel Target Detection for Multispectral Imagery","volume":"39","author":"Chang","year":"2000","journal-title":"Opt. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1109\/TGRS.2003.813704","article-title":"A Comparative Study for Orthogonal Subspace Projection and Constrained Energy Minimization","volume":"41","author":"Du","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1109\/TIT.1960.1057571","article-title":"An Introduction to Matched Filters","volume":"6","author":"Turin","year":"1960","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_46","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_47","first-page":"55","article-title":"Leveraging the High Dimensionality of AVIRIS Data for Improved Sub-Pixel Target Unmixing and Rejection of False Positives: Mixture Tuned Matched Filtering","volume":"Volume 97","author":"Boardman","year":"1998","journal-title":"Proceedings of the Summaries of the Seventh JPL Airborne Geoscience Workshop"},{"key":"ref_48","unstructured":"Joseph, W. (1994, January 9\u201312). Automated Spectral Analysis: A Geologic Example Using AVIRIS Data, North Grapevine Mountains, Nevada. Proceedings of the Tenth Thematic Conference on Geologic Remote Sensing, Environmental Research Institute of Michigan, San Antonio, TX, USA."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1109\/36.298007","article-title":"Hyperspectral Image Classification and Dimensionality Reduction: An Orthogonal Subspace Projection Approach","volume":"32","author":"Harsanyi","year":"1994","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1109\/36.673697","article-title":"Further Results on Relationship between Spectral Unmixing and Subspace Projection","volume":"36","author":"Chang","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","unstructured":"Chang, C.-I. (2003). Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Springer Science & Business Media."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The Spectral Image Processing System (SIPS)\u2014Interactive Visualization and Analysis of Imaging Spectrometer Data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1850","DOI":"10.1117\/1.1571062","article-title":"Constrained Energy Minimization and the Target-Constrained Interference-Minimized Filter","volume":"42","author":"Johnson","year":"2003","journal-title":"Opt. Eng."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3138","DOI":"10.1117\/1.1327499","article-title":"Target-Constrained Interference-Minimized Approach to Subpixel Target Detection for Hyperspectral Images","volume":"39","author":"Ren","year":"2000","journal-title":"Opt. Eng."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Jin, X., Paswaters, S., and Cline, H. (2009, January 1). A Comparative Study of Target Detection Algorithms for Hyperspectral Imagery. Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, Orlando, FL, USA.","DOI":"10.1117\/12.818790"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"095040","DOI":"10.1117\/1.JRS.9.095040","article-title":"Object-Oriented and Pixel-Based Classification Approach for Land Cover Using Airborne Long-Wave Infrared Hyperspectral Data","volume":"9","author":"Marwaha","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Shang, K., Xiao, C., and Liang, S. (2019, January 2\u20133). Comparison of Bare Soil Extraction Methods in Black Soil Zone for AHSI\/GF-5 Remote Sensing Data. Proceedings of the MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, Wuhan, China.","DOI":"10.1117\/12.2539360"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Jawak, S.D., Wankhede, S.F., Luis, A.J., and Balakrishna, K. (2022). Multispectral Characteristics of Glacier Surface Facies (Chandra-Bhaga Basin, Himalaya, and Ny-\u00c5lesund, Svalbard) through Investigations of Pixel and Object-Based Mapping Using Variable Processing Routines. Remote Sens., 14.","DOI":"10.3390\/rs14246311"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Jawak, S.D., Wankhede, S.F., Luis, A.J., and Balakrishna, K. (2022). Impact of Image-Processing Routines on Mapping Glacier Surface Facies from Svalbard and the Himalayas Using Pixel-Based Methods. Remote Sens., 14.","DOI":"10.3390\/rs14061414"},{"key":"ref_60","first-page":"102154","article-title":"Improved K-Means and Spectral Matching for Hyperspectral Mineral Mapping","volume":"91","author":"Ren","year":"2020","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Ren, Z., Zhai, Q., and Sun, L. (2022). A Novel Method for Hyperspectral Mineral Mapping Based on Clustering-Matching and Nonnegative Matrix Factorization. Remote Sens., 14.","DOI":"10.3390\/rs14041042"},{"key":"ref_62","unstructured":"Monastero, F.C. (2002). An Overview of Industry-Military Cooperation in the Development of Power Operations at the Coso Geothermal Field in Southern California. GRC Bull., 188\u2013194."},{"key":"ref_63","first-page":"585","article-title":"The Geologic Framework of the West Flank FORGE Site","volume":"40","author":"Siler","year":"2016","journal-title":"GRC Trans."},{"key":"ref_64","unstructured":"Whitmarsh, R. (2024, March 26). Geologic Map of the Coso Range. Available online: https:\/\/www.geosociety.org\/maps\/1998-whitmarsh-coso\/?WebsiteKey=a5b62ffc-18e7-49eb-b75a-db5406bdc7ea."},{"key":"ref_65","unstructured":"Erika (2022). Indicator Mineral Mapping for Geothermal Sites Using Multi\/Hyperspectral Imagery, Colorado School of Mines. Available online: https:\/\/repository.mines.edu\/bitstream\/handle\/11124\/15389\/Erika_mines_0052N_12355.pdf?sequence=1&isAllowed=y."},{"key":"ref_66","unstructured":"(2023, July 10). NASA LP DAAC ASTER Level 1 Precision Terrain Corrected Registered At-Sensor Radiance V003 [Data Set], Available online: https:\/\/lpdaac.usgs.gov\/products\/ast_l1tv003\/."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.gexplo.2018.02.006","article-title":"A Review of PXRF (Field Portable X-Ray Fluorescence) Applications for Applied Geochemistry","volume":"188","year":"2018","journal-title":"J. Geochem. Explor."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Laperche, V., and Lemi\u00e8re, B. (2020). Possible Pitfalls in the Analysis of Minerals and Loose Materials by Portable XRF, and How to Overcome Them. Minerals, 11.","DOI":"10.3390\/min11010033"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"121395","DOI":"10.1016\/j.chemgeo.2023.121395","article-title":"Portable X-Ray Fluorescence Calibrations: Workflow and Guidelines for Optimizing the Analysis of Geological Samples","volume":"623","author":"Triantafyllou","year":"2023","journal-title":"Chem. Geol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1180\/0026461067050351","article-title":"Scanning Transmission Electron Microscopy Using a SEM: Applications to Mineralogy and Petrology","volume":"70","author":"Lee","year":"2006","journal-title":"Miner. Mag."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.mineng.2008.11.003","article-title":"Research in Quantitative Mineralogy: Examples from Diverse Applications","volume":"22","author":"Hoal","year":"2009","journal-title":"Min. Eng."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Schulz, B., Sandmann, D., and Gilbricht, S. (2020). Sem-Based Automated Mineralogy and Its Application in Geo-and Material Sciences. Minerals, 10.","DOI":"10.3390\/min10111004"},{"key":"ref_73","unstructured":"Berman, M., Bischof, L., and Huntington, J. (1999, January 1\u20133). Algorithms and Software for the Automated Identification of Minerals Using Field Spectra or Hyperspectral Imagery. Proceedings of the 13th International Conference on Applied Geologic Remote Sensing, Vancouver, BC, Canada."},{"key":"ref_74","unstructured":"Berman, M., Bischof, L., Lagerstrom, R., Guo, Y., Huntington, J., and Mason, P. (2011). An Unmixing Algorithm Based on a Large Library of Shortwave Infrared Spectra, CSIRO Publishing."},{"key":"ref_75","first-page":"939","article-title":"Short Wavelength Infrared (SWIR) Spectral Analysis OfHydrothermal Alteration Zones Associated with Base Metal Sulfide Depositsat Rosebery and Western Tharsis, Tasmania, and Highway-Reward, Queensland","volume":"96","author":"Herrmann","year":"2001","journal-title":"Econ. Geol."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1109\/MGRS.2021.3071158","article-title":"Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review","volume":"9","author":"Borsoi","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"47","DOI":"10.3190\/jgeosci.250","article-title":"Automated Mineralogy and Petrology\u2014Applications of TESCAN Integrated Mineral Analyzer (TIMA)","volume":"63","author":"Hrstka","year":"2018","journal-title":"J. Geosci."},{"key":"ref_78","unstructured":"Bernstein, L.S., Adler-Golden, S.M., Sundberg, R.L., Levine, R.Y., Perkins, T.C., and Berk, A. (2005, January 25\u201329). A New Method for Atmospheric Correction and Aerosol Optical Property Retrieval for VIS-SWIR Multi-and Hyperspectral Imaging Sensors: QUAC (QUick Atmospheric Correction). Proceedings of the International Geoscience and Remote Sensing Symposium, Seoul, Republic of Korea."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1117\/12.603359","article-title":"Validation of the QUick Atmospheric Correction (QUAC) Algorithm for VNIR-SWIR Multi- and Hyperspectral Imagery","volume":"Volume 5806","author":"Bernstein","year":"2005","journal-title":"Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"111719","DOI":"10.1117\/1.OE.51.11.111719","article-title":"Quick Atmospheric Correction Code: Algorithm Description and Recent Upgrades","volume":"51","author":"Bernstein","year":"2012","journal-title":"Opt. Eng."},{"key":"ref_81","unstructured":"Scharf, L.L., and Mcwhorter, L.T. (1996, January 3\u20136). Adaptive Matched Subspace Detectors and Adaptive Coherence Estimators. Proceedings of the Conference Record of the Thirtieth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1109\/TAES.1986.310745","article-title":"An Adaptive Detection Algorithm","volume":"AES-22","author":"Kelly","year":"1986","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_83","unstructured":"Moraga, J., and Duzgun, H.S. (2022). JigsawHSI: A Network for Hyperspectral Image Classification. arXiv."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Messer, N., Ezekiel, S., Ferris, M.H., Blasch, E., Alford, M., Cornacchia, M., and Bubalo, A. (2015, January 13\u201315). ROC Curve Analysis for Validating Objective Image Fusion Metrics. Proceedings of the 2015 IEEE Applied Imagery Pattern Recognition Workshop, Washington, DC, USA.","DOI":"10.1109\/AIPR.2015.7444531"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/7\/1223\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:21:24Z","timestamp":1760106084000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/7\/1223"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,30]]},"references-count":84,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["rs16071223"],"URL":"https:\/\/doi.org\/10.3390\/rs16071223","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,30]]}}}