Abstract
Manually inspecting and analyzing satellite images can lead to numerous errors and is quite time consuming. Our geological contribution is to offer a means for the automatic classification of areas with felsic, mafic, and ultramafic rocks via machine learning using satellite images from Palmira and La Victoria, Colombia. Specifically, this study focuses on two types of satellite images taken from the Earth Observation System (EOS), namely natural color (bands B04 B03 B02) and infrared color vegetation (B08 B04 B03). The following machine learning algorithms were used in this study: Random Forest, K-Nearest Neighbors, Support Vector Machines, Logistic Regression, and Multilayer Perceptron. The model generated with K-Nearest Neighbors performed best for classifying natural color images with an accuracy of 91%, a precision of 87%, and a recall of 88%. Random Forest was the best model for classifying infrared images with an overall accuracy of 83%, a precision of 31%, and a recall of 31%.
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References
Earth Observing Data Analytics Inc.: EOS LandViewer: browse real-time Earth observation (2021). https://eos.com/products/landviewer/
Asokan, A., Anitha, J.: Machine learning based image processing techniques for satellite image analysis—a survey. In: 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon) (2019)
Baghdadi, N., Zribi, M., Mallet, C.: QGIS and Generic Tools. ISTE LTD. ISBN: 978-1-78630-187-1 (2018)
Bérubé, C.L., et al.: Predicting rock type and detecting hydrothermal alteration using machine learning and petrophysical properties of the Canadian malartic ore and host rocks, Pontiac Subprovince, Québec, Canada. Ore Geol. Rev. 96, 130–145 (2018)
Chakouri, M.: Geological and mineralogical mapping in Moroccan central Jebilet using multispectral and hyperspectral satellite data and machine learning. Int. J. Adv. Trends Comput. Sci. Eng. 9(4), 5772–5783 (2020)
Lu, D., Weng, Q.: A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 28(5), 823–870 (2007). https://www.tandfonline.com/doi/pdf/10.1080/01431160600746456?needAccess=true
Dhingra, S., Kumar, D.: A review of remotely sensed satellite image classification. Int. J. Electr. Comput. Eng. 9, 1720 (2019)
IBM Cloud Education: What is Random Forest? (2021). https://www.ibm.com/cloud/learn/random-forest
Encyclopedia Britannica: Felsic and mafic rocks - igneous rock (2021). https://www.britannica.com/science/felsic-rock
Garosi, Y., Sheklabadi, M., Conoscenti, C., Pourghasemi, H.R., Oost, K.V.: Assessing the performance of GIS-based machine learning models with different accuracy measures for determining susceptibility to gully erosion. Sci. Total Environ. 664, 1117–1132 (2019)
Gavade, A.B., Rajpurohit, V.S.: Systematic analysis of satellite image-based land cover classification techniques: literature review and challenges. Int. J. Comput. Appl. 43(6), 514–523 (2019)
GDAL/OGR: GDAL/OGR Geospatial Data Abstraction Software Library, Open Source Geospatial Foundation. https://gdal.org (2021)
Geologyin: How to classify igneous rocks into (ultramafic, mafic, intermediate and felsic)? (2021). https://www.geologyin.com/2014/12/how-to-classify-igneous-rocks-into.html
Geron, A.: Hands-on Machine Learning With SciKit-Learn and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems. O’Reilly Media Inc, Sebastopol, CA (2017)
Grosse, R.: Lecture 5: Multilayer perceptrons (2018). http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/readings/L05%20Multilayer%20Perceptrons.pdf
Liao, X., Huang, X., Huang, W.: ML-LUM: a system for land use mapping by machine learning algorithms. J. Comput. Lang. 54, 100908 (2019)
Nelson, D.: What is a KNN (k-nearest neighbors)? (2020). https://www.unite.ai/what-is-k-nearest-neighbors/
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 3426 (2011)
Korstanje, J.: The k-nearest neighbors (kNN) Algorithm in Python (2021). https://realpython.com/knn-python/
Rodríguez Ramos, B.P.: Estudio metalgenético de las mineralizaciones auríferas del área de Ginebra y zonas aledañas, Valle del Cauca Universidad Nacional de Colombia (2012). https://repositorio.unal.edu.co/handle/unal/21336
Tangthaikwan, K., Keeratipranon, N., Agsornintara, A.: Multiclass support vector machine for classification spatial data from satellite image. Multiclass support vector machine for classification spatial data from satellite image (2017)
Pennsylvania State University: 12.1—logistic regression | stat 462 2021 (2021). Available: https://online.stat.psu.edu/stat462/node/207/
Vera Torres, J.A.: RACEFN Glosario de Geología (2009). http://www.ugr.es/~agcasco/personal/rac-geologia/0-rac.htm
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Bosquez, S., Alférez, G.H., Ardila, A.M.M., Clausen, B.L. (2022). Automatic Classification of Felsic, Mafic, and Ultramafic Rocks in Satellite Images from Palmira and La Victoria, Colombia. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 507. Springer, Cham. https://doi.org/10.1007/978-3-031-10464-0_36
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