* Latifovic et al. (2018) Latifovic, R., Pouliot, D., Campbell, J., 2018.  Assessment of convolution neural networks for surficial geology mapping in the South Rae geological region, Northwest Territories, Canada.  Remote Sensing 10, 307\.  doi:10.3390/rs10020307.
  * Leach et al. (2005) Leach, D., Sangster, D., Kelley, K., Large, R., Garven, G., Allen, C., Gutzmer, J., Walters, S., 2005\.  Sediment-hosted lead-zinc deposits: A global perspective.  Economic Geology 100th Anni, 561–607.
  * LeCun et al. (1988) LeCun, Y., Touresky, D., Hinton, G., Sejnowski, T., 1988\.  A theoretical framework for back-propagation, in: Connectionist Models Summer School, pp. 21–28.
  * Lecun and Yoshua (1998) Lecun, Y., Yoshua, B., 1998\.  Convolutional networks for images, speech, and time series In: The Handbook of Brain Theory and Neural Networks.
  * Lee et al. (2020) Lee, Z., Churnside, J., Mao, Z., Wu, S., Zibordi, G., 2020.  Active and passive optical remote sensing of the aquatic environment: Introduction to the feature issue.  Applied Optics 59, APS1–APS2.  doi:10.1364/AO.392549.
  * Lek and Guegan (1999) Lek, S., Guegan, J., 1999.  Artificial neural networks as a tool in ecological modelling, an introduction.  Ecological Modelling 120 (2-3), 65–73.
  * Leverington (2010) Leverington, D.W., 2010.  Discrimination of sedimentary lithologies using Hyperion and Landsat Thematic Mapper data: A case study at Melville Island, Canadian High Arctic.  International Journal of Remote Sensing 31, 233–260.  doi:10.1080/01431160902882637.
  * Leverington and Moon (2012) Leverington, D.W., Moon, W.M., 2012\.  Landsat-TM-based discrimination of lithological units associated with the Purtuniq ophiolite, Quebec, Canada.  Remote Sensing 4, 1208–1231.  doi:10.3390/rs4051208.
  * Li et al. (2020a) Li, H.C., Hu, W.S., Li, W., Li, J., Du, Q., Plaza, A., 2020a.  A3CLNN: Spatial, spectral and multiscale attention convlstm neural network for multisource remote sensing data classification.  IEEE Transactions on Neural Networks and Learning Systems , 1–15doi:10.1109/TNNLS.2020.3028945.
  * Li et al. (2018) Li, N., Huang, X., Zhao, H., Qiu, X., Geng, R., Jia, X., Wang, D., 2018.  Multiparameter optimization for mineral mapping using hyperspectral imagery.  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, 1348–1357.  doi:10.1109/JSTARS.2018.2814617.
  * Li et al. (2020b) Li, Y., Chen, R., Zhang, Y., Zhang, M., Chen, L., 2020b.  Multi-label remote sensing image scene classification by combining a convolutional neural network and a graph neural network.  Remote Sensing 12, 4003\.  doi:10.3390/rs12234003.
  * Lin et al. (2020) Lin, N., Chen, Y., Lu, L., 2020.  Mineral potential mapping using a conjugate gradient logistic regression model.  Natural Resources Research 29, 173–188.  doi:10.1007/s11053-019-09509-1.
  * Liu (2015) Liu, P., 2015.  A survey of remote-sensing big data.  Frontiers in Environmental Science 3\.  doi:10.3389/fenvs.2015.00045.
  * Lloyd (1982) Lloyd, S., 1982.  Least squares quantization in PCM.  IEEE Transactions on Information Theory 28, 129–137.  doi:10.1109/TIT.1982.1056489.
  * Loew et al. (2017) Loew, A., Bell, W., Brocca, L., Bulgin, C.E., Burdanowitz, J., Calbet, X., Donner, R.V., Ghent, D., Gruber, A., Kaminski, T., Kinzel, J., Klepp, C., Lambert, J.C., Schaepman-Strub, G., Schröder, M., Verhoelst, T., 2017\.  Validation practices for satellite-based earth observation data across communities.  Reviews of Geophysics 55, 779–817.  doi:10.1002/2017RG000562.
  * Lorenz et al. (2021) Lorenz, S., Ghamisi, P., Kirsch, M., Jackisch, R., Rasti, B., Gloaguen, R., 2021\.  Feature extraction for hyperspectral mineral domain mapping: A test of conventional and innovative methods.  Remote Sensing of Environment 252, 112129.  doi:10.1016/j.rse.2020.112129.
  * Lowell and Guilbert (1970) Lowell, J.D., Guilbert, J.M., 1970\.  Lateral and vertical alteration-mineralization zoning in porphyry ore deposits.  Economic Geology 65, 373–408.  doi:10.2113/gsecongeo.65.4.373.
  * Ma et al. (2017) Ma, J., Xiao, X., Qin, Y., Chen, B., Hu, Y., Li, X., Zhao, B., 2017.  Estimating aboveground biomass of broadleaf, needleleaf, and mixed forests in northeastern China through analysis of 25-m ALOS/PALSAR mosaic data.  Forest Ecology and Management 389, 199–210.  doi:10.1016/j.foreco.2016.12.020.
  * Mahanta and Maiti (2018) Mahanta, P., Maiti, S., 2018\.  Regional scale demarcation of alteration zone using ASTER imageries in South Purulia Shear Zone, East India: Implication for mineral exploration in vegetated regions.  Ore Geology Reviews 102, 846–861.  doi:10.1016/j.oregeorev.2018.07.028.
  * Mansouri et al. (2018) Mansouri, E., Feizi, F., Jafari Rad, A., Arian, M., 2018\.  Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: A case study in the Sarvian area, Central Iran.  Solid Earth 9, 373–384.  doi:10.5194/se-9-373-2018.
  * Maxwell et al. (2018) Maxwell, A.E., Warner, T.A., Fang, F., 2018.  Implementation of machine-learning classification in remote sensing: An applied review.  International Journal of Remote Sensing 39, 2784–2817.  doi:10.1080/01431161.2018.1433343.
  * van der Meer (1994) van der Meer, F., 1994.  Extraction of mineral absorption features from high-spectralresolution data using non-parametric geostatistical techniques.  International Journal of Remote Sensing 15, 2193–2214.  doi:10.1080/01431169408954238.
  * van der Meer et al. (2012) van der Meer, F.D., van der Werff, H.M., van Ruitenbeek, F.J., Hecker, C.A., Bakker, W.H., Noomen, M.F., van der Meijde, M., Carranza, E.J.M., de Smeth, J.B., Woldai, T., 2012\.  Multi- and hyperspectral geologic remote sensing: A review.  International Journal of Applied Earth Observation and Geoinformation 14, 112–128.  doi:10.1016/j.jag.2011.08.002.
  * Metelka et al. (2018) Metelka, V., Baratoux, L., Jessell, M.W., Barth, A., Ježek, J., Naba, S., 2018\.  Automated regolith landform mapping using airborne geophysics and remote sensing data, Burkina Faso, West Africa.  Remote Sensing of Environment 204, 964–978.  doi:10.1016/j.rse.2017.08.004.
  * Micklethwaite et al. (2010) Micklethwaite, S., Sheldon, H.A., Baker, T., 2010.  Active fault and shear processes and their implications for mineral deposit formation and discovery.  Journal of Structural Geology 32, 151–165.  doi:10.1016/j.jsg.2009.10.009.
  * Navarro et al. (1997) Navarro, J.F., Frenk, C.S., White, S.D., 1997.  A universal density profile from hierarchical clustering.  The Astrophysical Journal 490, 493–508.  doi:10.1086/304888.
  * Nielsen (2011) Nielsen, A.A., 2011.  Kernel maximum autocorrelation factor and minimum noise fraction transformations.  IEEE Transactions on Image Processing 20, 612–624.  doi:10.1109/TIP.2010.2076296.
  * Ninomiya and Fu (2019) Ninomiya, Y., Fu, B., 2019.  Thermal infrared multispectral remote sensing of lithology and mineralogy based on spectral properties of materials.  Ore Geology Reviews 108, 54–72.  doi:10.1016/j.oregeorev.2018.03.012.
  * Ninomiya et al. (2005) Ninomiya, Y., Fu, B., Cudahy, T.J., 2005.  Detecting lithology with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral thermal infrared ”radiance-at-sensor” data.  Remote Sensing of Environment 99, 127–139.  doi:10.1016/j.rse.2005.06.009.
  * Noori et al. (2019) Noori, L., Beiranvand Pour, A., Askari, G., Taghipour, N., Pradhan, B., Lee, C.W., Honarmand, M., 2019.  Comparison of different algorithms to map hydrothermal alteration zones using ASTER remote sensing data for polymetallic vein-type ore exploration: Toroud–Chahshirin magmatic belt (TCMB), North Iran.  Remote Sensing 11, 495\.  doi:10.3390/rs11050495.
  * Olierook et al. (2021) Olierook, H.K., Scalzo, R., Kohn, D., Chandra, R., Farahbakhsh, E., Clark, C., Reddy, S.M., Müller, R.D., 2021\.  Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models.  Geoscience Frontiers 12, 479–493.  doi:10.1016/j.gsf.2020.04.015.
  * Olson (1995) Olson, C.F., 1995.  Parallel algorithms for hierarchical clustering.  Parallel Computing 21, 1313–1325.  doi:10.1016/0167-8191(95)00017-I.
  * Othman and Gloaguen (2014) Othman, A., Gloaguen, R., 2014\.  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).  Remote Sensing 6, 6867–6896.  doi:10.3390/rs6086867.
  * Pal et al. (2020) Pal, M., Rasmussen, T., Porwal, A., 2020.  Optimized lithological mapping from multispectral and hyperspectral remote sensing images using fused multi-classifiers.  Remote Sensing 12, 177\.  doi:10.3390/rs12010177.
  * Pan et al. (2019) Pan, E., Ma, Y., Mei, X., Dai, X., Fan, F., Tian, X., Ma, J., 2019.  Spectral-spatial classification of hyperspectral image based on a joint attention network, in: IEEE International Geoscience and Remote Sensing Symposium, pp. 413–416.  doi:10.1109/IGARSS.2019.8898758.
  * Park and Choi (2020) Park, S., Choi, Y., 2020.  Applications of unmanned aerial vehicles in mining from exploration to reclamation: A review.  Minerals 10, 663\.  doi:10.3390/min10080663.
  * Pearlman et al. (2003) Pearlman, J., Barry, P., Segal, C., Shepanski, J., Beiso, D., Carman, S., 2003\.  Hyperion, a space-based imaging spectrometer.  IEEE Transactions on Geoscience and Remote Sensing 41, 1160–1173.  doi:10.1109/TGRS.2003.815018.
  * Peng and Gao (2013) Peng, G., Gao, G., 2013.  Remote sensing prospecting of pegmatite deposits in the azubai region, xinjiang.  Geotectonica et Metallogenia 37, 109–117.
  * Porwal et al. (2006) Porwal, A., Carranza, E., Hale, M., 2006.  Bayesian network classifiers for mineral potential mapping.  Computers & Geosciences 32, 1–16.  doi:10.1016/j.cageo.2005.03.018.
  * Prost (2014) Prost, G.L., 2014.  Remote Sensing for Geoscientists: Image Analysis and Integration.  3rd ed., CRC Press.
  * Pu et al. (2016) Pu, Y., Gan, Z., Henao, R., Yuan, X., Li, C., Stevens, A., Carin, L., 2016\.  Variational autoencoder for deep learning of images, labels and captions.  arXiv:1609.08976.
  * Quinlan (1986) Quinlan, J.R., 1986.  Induction of decision trees.  Machine learning 1, 81–106.  doi:10.1007/BF00116251.
  * Quinlan (1987) Quinlan, J.R., 1987.  Simplifying decision trees.  International Journal of Man-Machine Studies 27, 221–234.  doi:10.1016/S0020-7373(87)80053-6.
  * Radford et al. (2018) Radford, D.D., Cracknell, M.J., Roach, M.J., Cumming, G.V., 2018\.  Geological mapping in Western Tasmania using radar and random forests.  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, 3075–3087.  doi:10.1109/JSTARS.2018.2855207.
  * Raharimahefa and Kusky (2009) Raharimahefa, T., Kusky, T.M., 2009\.  Structural and remote sensing analysis of the Betsimisaraka Suture in northeastern Madagascar.  Gondwana Research 15, 14–27.  doi:10.1016/j.gr.2008.07.004.
  * Rajan Girija and Mayappan (2019) Rajan Girija, R., Mayappan, S., 2019\.  Mapping of mineral resources and lithological units: A review of remote sensing techniques.  International Journal of Image and Data Fusion 10, 79–106.  doi:10.1080/19479832.2019.1589585.
  * Rajesh (2004) Rajesh, H., 2004.  Application of remote sensing and GIS in mineral resource mapping-An overview.  Journal of Mineralogical and Petrological Sciences 99, 83–103.  doi:10.2465/jmps.99.83.
  * Ren et al. (2019) Ren, Z., Sun, L., Zhai, Q., Liu, X., 2019.  Mineral mapping with hyperspectral image based on an improved k-means clustering algorithm, in: IEEE International Geoscience and Remote Sensing Symposium, pp. 2989–2992.  doi:10.1109/IGARSS.2019.8899113.
  * Rendu (1976) Rendu, J., 1976.  Bayesian decision theory applied to mineral exploration and mine valuation, in: Advanced Geostatistics in the Mining Industry, Springer Netherlands. pp. 435–445.
  * Rezaei et al. (2020) Rezaei, A., Hassani, H., Moarefvand, P., Golmohammadi, A., 2020\.  Lithological mapping in Sangan region in Northeast Iran using ASTER satellite data and image processing methods.  Geology, Ecology, and Landscapes 4, 59–70.  doi:10.1080/24749508.2019.1585657.
  * Richards (2003) Richards, J., 2003.  Tectono-Magmatic Precursors for Porphyry Cu-(Mo-Au) Deposit Formation.  Economic Geology 98, 1515–1533.  doi:10.2113/98.8.1515.
  * Richards and Jia (2006) Richards, J.A., Jia, X., 2006\.  Remote Sensing Digital Image Analysis.  4th ed., Springer: Berlin/Heidelberg, Germany.
  * Richards (2011) Richards, J.P., 2011.  Magmatic to hydrothermal metal fluxes in convergent and collided margins.  Ore Geology Reviews 40, 1–26.  doi:10.1016/j.oregeorev.2011.05.006.
  * Rigol-Sanchez et al. (2003) Rigol-Sanchez, J., Chica-Olmo, M., Abarca-Hernandez, F., 2003.  Artificial neural networks as a tool for mineral potential mapping with GIS.  International Journal of Remote Sensing 24, 1151–1156.  doi:10.1080/0143116021000031791.
  * Ripley and Li (2018) Ripley, E.M., Li, C., 2018.  Metallic ore deposits associated with mafic to ultramafic igneous rocks, in: Processes and Ore Deposits of Ultramafic-Mafic Magmas through Space and Time. Elsevier, pp. 79–111.  doi:10.1016/B978-0-12-811159-8.00004-4.
  * Rokach (2010) Rokach, L., 2010.  Ensemble-based classifiers.  Artificial Intelligence Review 33, 1–39.  doi:10.1007/s10462-009-9124-7.
  * Rowan and Mars (2003) Rowan, L.C., Mars, J.C., 2003\.  Lithologic mapping in the Mountain Pass, California area using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data.  Remote Sensing of Environment 84, 350–366.  doi:10.1016/S0034-4257(02)00127-X.
  * Rowan et al. (2006) Rowan, L.C., Schmidt, R.G., Mars, J.C., 2006.  Distribution of hydrothermally altered rocks in the Reko Diq, Pakistan mineralized area based on spectral analysis of ASTER data.  Remote Sensing of Environment 104, 74–87.  doi:10.1016/j.rse.2006.05.014.
  * Ruiz et al. (2014) Ruiz, P., Mateos, J., Camps-Valls, G., Molina, R., Katsaggelos, A.K., 2014.  Bayesian active remote sensing image classification.  IEEE Transactions on Geoscience and Remote Sensing 52, 2186–2196.  doi:10.1109/TGRS.2013.2258468.
  * Rumelhart et al. (1986) Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986.  Learning representations by back-propagating errors.  nature 323, 533–536.  doi:10.1038/323533a0.
  * Sabins (1999) Sabins, F.F., 1999.  Remote sensing for mineral exploration.  Ore Geology Reviews 14, 157–183.  doi:10.1016/S0169-1368(99)00007-4.
  * Sagi and Rokach (2018) Sagi, O., Rokach, L., 2018.  Ensemble learning: A survey.  Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8.  doi:10.1002/widm.1249.
  * Salazar and Coffman (2020) Salazar, S.E., Coffman, R.A., 2020\.  Validation of a ground-based telescope-assisted hyperspectral remote sensor for soil measurements.  Journal of Applied Remote Sensing 14, 1–13.  doi:10.1117/1.JRS.14.027503.
  * Sang et al. (2020) Sang, X., Xue, L., Ran, X., Li, X., Liu, J., Liu, Z., 2020.  Intelligent high-resolution geological mapping based on SLIC-CNN.  ISPRS International Journal of Geo-Information 9, 99.  doi:10.3390/ijgi9020099.
  * Schmidhuber (2015) Schmidhuber, J., 2015.  Deep learning in neural networks: An overview.  Neural Networks 61, 85–117.  doi:10.1016/j.neunet.2014.09.003.
  * Sekandari et al. (2020) Sekandari, M., Masoumi, I., Beiranvand Pour, A., M Muslim, A., Rahmani, O., Hashim, M., Zoheir, B., Pradhan, B., Misra, A., 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 Sensing 12, 1239\.  doi:10.3390/rs12081239.
  * Shamsolmoali et al. (2020) Shamsolmoali, P., Zareapoor, M., Shen, L., Sadka, A.H., Yang, J., 2020.  Imbalanced data learning by minority class augmentation using capsule adversarial networks.  Neurocomputing doi:10.1016/j.neucom.2020.01.119.
  * Sheikhrahimi et al. (2019) Sheikhrahimi, A., Beiranvand Pour, A., Pradhan, B., Zoheir, B., 2019\.  Mapping hydrothermal alteration zones and lineaments associated with orogenic gold mineralization using ASTER data: A case study from the Sanandaj-Sirjan Zone, Iran.  Advances in Space Research 63, 3315–3332.  doi:10.1016/j.asr.2019.01.035.
  * Shi et al. (2017) Shi, J., Chen, J., Zhu, J., Sun, S., Luo, Y., Gu, Y., Zhou, Y., 2017.  Zhusuan: A library for bayesian deep learning.  arXiv:1709.05870.
  * Shi et al. (2015) Shi, X., Chen, Z., Wang, H., Yeung, D.Y., kin Wong, W., chun Woo, W., 2015.  Convolutional lstm network: A machine learning approach for precipitation nowcasting.  arXiv:1506.04214.
  * Shirmard et al. (2020) Shirmard, H., Farahbakhsh, E., Beiranvand Pour, A., Muslim, A.M., Müller, R.D., Chandra, R., 2020\.  Integration of selective dimensionality reduction techniques for mineral exploration using ASTER satellite data.  Remote Sensing 12, 1261\.  doi:10.3390/rs12081261.
  * Shrestha and Mahmood (2019) Shrestha, A., Mahmood, A., 2019\.  Review of deep learning algorithms and architectures.  IEEE Access 7, 53040–53065.  doi:10.1109/ACCESS.2019.2912200.
  * Sillitoe (2010) Sillitoe, R.H., 2010.  Porphyry copper systems.  Economic Geology 105, 3–4.
  * Song et al. (2020) Song, K., Wang, E., Yao, Y., Fu, J., Hao, D., You, X., 2020.  Spectral alteration zonation based on close range hyspex-320 m imaging spectroscopy: A case study in the gongchangling high-grade iron ore deposit, liaoning province, ne china.  Applied Sciences 10, 8369\.  doi:10.3390/app10238369.
  * Song and Ying (2015) Song, Y.Y., Ying, L., 2015.  Decision tree methods: Applications for classification and prediction.  Shanghai Archives of Psychiatry 27, 130–135.  doi:10.11919/j.issn.1002-0829.215044.
  * Storvik et al. (2005) Storvik, G., Fjortoft, R., Solberg, A., 2005.  A bayesian approach to classification of multiresolution remote sensing data.  IEEE Transactions on Geoscience and Remote Sensing 43, 539–547.  doi:10.1109/TGRS.2004.841395.
  * Sudaryatno et al. (2020) Sudaryatno, Widayani, P., Wibowo, T.W., Pramono, B.A.S., Afifah, Z.N., Meikasari, A.D., Firdaus, M.R., 2020.  Multiple linear regression analysis of remote sensing data for determining vulnerability factors of landslide in Purworejo.  IOP Conference Series: Earth and Environmental Science 500, 012046.  doi:10.1088/1755-1315/500/1/012046.
  * Sun and Scanlon (2019) Sun, A.Y., Scanlon, B.R., 2019\.  How can big data and machine learning benefit environment and water management: A survey of methods, applications, and future directions.  Environmental Research Letters 14, 73001.  doi:10.1088/1748-9326/ab1b7d.
  * Sydow (1977) Sydow, A., 1977.  Pattern Recognition Principles.  Journal of Applied Mathematics and Mechanics 57, 353–354.  doi:10.1002/zamm.19770570626.
  * Tagnon et al. (2020) Tagnon, B.O., Assoma, V.T., Mangoua, J.M.O., Douagui, A.G., Kouamé, F.K., Savané, I., 2020\.  Contribution of SAR/RADARSAT-1 and ASAR/ENVISAT images to geological structural mapping and assessment of lineaments density in Divo-Oume Area (Côte d’Ivoire).  The Egyptian Journal of Remote Sensing and Space Science 23, 231–241.  doi:10.1016/j.ejrs.2018.12.001.
  * Tahmasebi et al. (2020) Tahmasebi, P., Kamrava, S., Bai, T., Sahimi, M., 2020\.  Machine learning in geo- and environmental sciences: From small to large scale.  Advances in Water Resources 142, 103619\.  doi:10.1016/j.advwatres.2020.103619.
  * Takodjou Wambo et al. (2020) Takodjou Wambo, J.D., Beiranvand Pour, A., Ganno, S., Asimow, P.D., Zoheir, B., Salles, R.d.R., Nzenti, J.P., Pradhan, B., Muslim, A.M., 2020.  Identifying high potential zones of gold mineralization in a sub-tropical region using Landsat-8 and ASTER remote sensing data: A case study of the Ngoura-Colomines goldfield, eastern Cameroon.  Ore Geology Reviews 122, 103530\.  doi:10.1016/j.oregeorev.2020.103530.
  * Tang et al. (2019) Tang, T., Chen, S., Zhao, M., Huang, W., Luo, J., 2019\.  Very large-scale data classification based on k-means clustering and multi-kernel svm.  Soft Computing 23, 3793–3801.  doi:10.1007/s00500-018-3041-0.
  * Testa et al. (2018) Testa, F., Villanueva, C., Cooke, D., Zhang, L., 2018\.  Lithological and hydrothermal alteration mapping of epithermal, porphyry and tourmaline breccia districts in the Argentine Andes using ASTER imagery.  Remote Sensing 10, 203\.  doi:10.3390/rs10020203.
  * Tewksbury et al. (2012) Tewksbury, B.J., Dokmak, A.A., Tarabees, E.A., Mansour, A.S., 2012\.  Google earth and geologic research in remote regions of the developing world: An example from the western desert of egypt.  Google Earth and Virtual Visualizations in Geoscience Education and Research: Geological Society of America Special Paper 492, 23–36.  doi:10.1130/2012.2492(02).
  * Toth and Jóźków (2016) Toth, C., Jóźków, G., 2016\.  Remote sensing platforms and sensors: A survey.  ISPRS Journal of Photogrammetry and Remote Sensing 115, 22–36.  doi:10.1016/j.isprsjprs.2015.10.004.
  * Traore et al. (2020) Traore, M., Takodjou Wambo, J.D., Ndepete, C.P., Tekin, S., Beiranvand Pour, A., Muslim, A.M., 2020\.  Lithological and alteration mineral mapping for alluvial gold exploration in the south east of Birao Area, Central African Republic using Landsat-8 operational land imager (OLI) data.  Journal of African Earth Sciences 170, 103933.  doi:10.1016/j.jafrearsci.2020.103933.
  * Tripathi et al. (2020) Tripathi, M.K., Govil, H., Chattoraj, S., 2020.  Identification of hydrothermal altered/weathered and clay minerals through airborne AVIRIS-NG hyperspectral data in Jahajpur, India.  Heliyon 6, e03487.  doi:10.1016/j.heliyon.2020.e03487.
  * Usui and Okamoto (2010) Usui, A., Okamoto, N., 2010\.  Geophysical and geological exploration of cobalt-rich ferromanganese crusts: An attempt of small-scale mapping on a micronesian seamount.  Marine Georesources and Geotechnology 28, 192–206.  doi:10.1080/10641190903521717.
  * Utgoff (1989) Utgoff, P.E., 1989.  Incremental induction of decision trees.  Machine Learning 4, 161–186.  doi:10.1023/A:1022699900025.
  * Varouchakis et al. (2021) Varouchakis, E., Kamińska-Chuchmała, A., Kowalik, G., Spanoudaki, K., Graña, M., 2021.  Combining geostatistics and remote sensing data to improve spatiotemporal analysis of precipitation.  Sensors 21.  doi:10.3390/s21093132.
  * Velliangiri et al. (2019) Velliangiri, S., Alagumuthukrishnan, S., Thankumar joseph, S.I., 2019.  A review of dimensionality reduction techniques for efficient computation.  Procedia Computer Science 165, 104–111.  doi:10.1016/j.procs.2020.01.079.
  * Von Luxburg (2007) Von Luxburg, U., 2007.  A tutorial on spectral clustering.  Statistics and computing 17, 395–416.  doi:10.1007/s11222-007-9033-z.
  * Wang et al. (2020a) Wang, B., Peng, X., Jiang, M., Liu, D., 2020a.  Real-time fault detection for UAV based on model acceleration engine.  IEEE Transactions on Instrumentation and Measurement 69, 9505–9516.  doi:10.1109/TIM.2020.3001659.
  * Wang et al. (2010) Wang, G., Yan, C., Zhang, S., Song, Y., 2010.  Probabilistic neural networks and fractal method applied to mineral potential mapping in Luanchuan region, Henan Province, China, in: Sixth International Conference on Natural Computation, pp. 1003–1007.  doi:10.1109/ICNC.2010.5582906.
  * Wang and Yeung (2020) Wang, H., Yeung, D.Y., 2020\.  A survey on bayesian deep learning.  ACM Computing Surveys 53, 1–38.  doi:10.1145/3409383.
  * Wang et al. (2016) Wang, S., Li, X., Ge, Y., Jin, R., Ma, M., Liu, Q., Wen, J., Liu, S., 2016\.  Validation of regional-scale remote sensing products in china: From site to network.  Remote Sensing 8, 980\.  doi:10.3390/rs8120980.
  * Wang et al. (2014) Wang, W., Huang, Y., Wang, Y., Wang, L., 2014.  Generalized autoencoder: A neural network framework for dimensionality reduction, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 490–497.
  * Wang and Jiang (2019) Wang, Y., Jiang, Y., 2019.  A weighted minimum distance classifier based on relative offset, in: Fourth International Conference on Cloud Computing and Big Data Analysis, pp. 343–347.  doi:10.1109/ICCCBDA.2019.8725734.
  * Wang and Zheng (2010) Wang, Z., Zheng, C., 2010.  Rocks/minerals information extraction from EO-1 Hyperion data base on SVM, in: International Conference on Intelligent Computation Technology and Automation, pp. 229–232.  doi:10.1109/ICICTA.2010.341.
  * Wang et al. (2020b) Wang, Z., Zuo, R., Dong, Y., 2020b.  Mapping of Himalaya leucogranites based on ASTER and Sentinel-2A datasets using a hybrid method of metric learning and random forest.  IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, 1925–1936.  doi:10.1109/JSTARS.2020.2989509.
  * Werbos (1990) Werbos, P.J., 1990.  Backpropagation through time: what it does and how to do it.  Proceedings of the IEEE 78, 1550–1560.  doi:10.1109/5.58337.
  * Wold et al. (1987) Wold, S., Esbensen, K., Geladi, P., 1987.  Principal component analysis.  Chemometrics and Intelligent Laboratory Systems 2, 37–52.  doi:10.1016/0169-7439(87)80084-9.
  * Wu et al. (2019) Wu, X., Xiao, Q., Wen, J., You, D., Hueni, A., 2019\.  Advances in quantitative remote sensing product validation: Overview and current status.  Earth-Science Reviews 196, 102875\.  doi:10.1016/j.earscirev.2019.102875.
  * Wulder et al. (2008) Wulder, M.A., White, J.C., Goward, S.N., Masek, J.G., Irons, J.R., Herold, M., Cohen, W.B., Loveland, T.R., Woodcock, C.E., 2008.  Landsat continuity: Issues and opportunities for land cover monitoring.  Remote Sensing of Environment 112, 955–969.  doi:10.1016/j.rse.2007.07.004.
  * Xia et al. (2020) Xia, K., Huang, J., Wang, H., 2020.  Lstm-cnn architecture for human activity recognition.  IEEE Access 8, 56855–56866.  doi:10.1109/ACCESS.2020.2982225.
  * Xie et al. (2020) Xie, T., Liu, R., Wei, Z., 2020.  Improvement of the fast clustering algorithm improved by k-means in the big data.  Applied Mathematics and Nonlinear Sciences 5, 1–10.  doi:10.2478/amns.2020.1.00001.
  * Xu et al. (2019) Xu, K., Wang, X., Kong, C., Feng, R., Liu, G., Wu, C., 2019.  Identification of hydrothermal alteration minerals for exploring gold deposits based on SVM and PCA using ASTER data: A case study of Gulong.  Remote Sensing 11, 3003\.  doi:10.3390/rs11243003.
  * Yang et al. (2010) Yang, P., Hwa Yang, Y., B. Zhou, B., Y. Zomaya, A., 2010\.  A review of ensemble methods in bioinformatics.  Current Bioinformatics 5, 296–308.  doi:10.2174/157489310794072508.
  * Ye et al. (2017) Ye, B., Tian, S., Ge, J., Sun, Y., 2017.  Assessment of WorldView-3 data for lithological mapping.  Remote Sensing 9, 1132\.  doi:10.3390/rs9111132.
  * Yetkin et al. (2004) Yetkin, E., Toprak, V., Süzen, M., 2004.  Alteration mapping by remote sensing: Application to Hasandağ – Melendiz volcanic complex, Central Turkey, in: 20th ISPRS Congress on Technical Commission VII.
  * Yu et al. (2012) Yu, L., Porwal, A., Holden, E.J., Dentith, M.C., 2012\.  Towards automatic lithological classification from remote sensing data using support vector machines.  Computers & Geosciences 45, 229–239.  doi:10.1016/j.cageo.2011.11.019.
  * Zhang et al. (2020) Zhang, F., Hu, Z., Fu, Y., Yang, K., Wu, Q., Feng, Z., 2020.  A New Identification Method for Surface Cracks from UAV Images Based on Machine Learning in Coal Mining Areas.  Remote Sensing 12, 1571\.  doi:10.3390/rs12101571.
  * Zhang et al. (2016) Zhang, T., Yi, G., Li, H., Wang, Z., Tang, J., Zhong, K., Li, Y., Wang, Q., Bie, X., 2016.  Integrating data of ASTER and Landsat-8 OLI (AO) for hydrothermal alteration mineral mapping in Duolong porphyry Cu-Au deposit, Tibetan Plateau, China.  Remote Sensing 8, 890\.  doi:10.3390/rs8110890.
  * Zhao et al. (2020) Zhao, H., Deng, K., Li, N., Wang, Z., Wei, W., 2020\.  Hierarchical spatial-spectral feature extraction with long short term memory (LSTM) for mineral identification using hyperspectral imagery.  Sensors 20, 6854\.  doi:10.3390/s20236854.
  * Zhou and Guan (2011) Zhou, M.L., Guan, Z.Q., 2011\.  Active-passive microwave remote sensing data combination for retrieval of soil moisture, in: Proceedings of SPIE - The International Society for Optical Engineering.  doi:10.1117/12.912598.
  * Zhu and Goldberg (2009) Zhu, X., Goldberg, A.B., 2009\.  Introduction to semi-supervised learning.  Synthesis Lectures on Artificial Intelligence and Machine Learning 3, 1–130.  doi:10.2200/S00196ED1V01Y200906AIM006.
  * Zoheir et al. (2019) Zoheir, B., Emam, A., Abdel-Wahed, M., Soliman, N., 2019\.  Multispectral and radar data for the setting of gold mineralization in the Southeastern Desert, Egypt.  Remote Sensing 11, 1450\.  doi:10.3390/rs11121450.