Identification of Lithology Using Sentinel-2A Through an Ensemble of Machine Learning Algorithms

Abstract

Remotely sensed data has become an effective, operative and applicable tool that provide critical support for geological surveys and studies by reducing the costs and increasing the precision. Advances in remote-sensing data analysis methods, like machine learning algorithms, allow for easy and impartial geological mapping. This study aims to carry out a rigorous comparison of the performance of three supervised classification methods: Random Forest, k-Nearest Neighbor and maximum likelihood using remote sensing data and additional information in Souk El Had N’Befourna region. The enhancement of remote sensing geological classification by using geomorphometric features, principal component analysis, gray level co-occurrence matrix (GLCM) and multispectral data of the Sentinel-2A imagery was highlighted. The Random Forest algorithm showed reliable results and discriminated limestone, dolomite, conglomerate, sandstone and rhyolite, silt and Alluvium, ignimbrite, granodiorite, Lutite, granite, and quartzite. The best overall accuracy (~91%) was achieved by Random Forest algorithm.

A review on advancements in lithological mapping utilizing machine learning algorithms and remote sensing data

“… [ 80 ] SVM/Gamma-ray spectrometric measurements of K, Th, and U Sentinel-2 Including a single chemical concentration (K, Th, or U) in the allocation enhances results compared to using remote sensing data alone. It boosts the Overall Accuracy by 4.14%, 5.11%, and 6.83% when U, K, and Th are added, respectively [ 63 ] RF, KNN &MLE Sentinel-2 The random forest algorithm yielded the highest overall accuracy of around 91% for geological classification in the studied region [ 34 ] SVM/PCA/MNF/BR Sentinel-2/ASTER Sentinel-2A data outperformed ASTER in lithological mapping. MNF, could highlight specific rock units and improve classification accuracy [ 64 ] RF ASTER The study demonstrates the effectiveness of the RF classifier in lithological mapping using ASTER imagery with an increased overall accuracy of up to 81.52%.…”

“…Bachri et al (2022) [ 63 ] conducted a study with the objective of identifying lithology (mineralogical composition of rocks) in the Souk Arbaa Sahel region, Sidi Ifni Inlier, Western Anti-Atlas. They employed Sentinel-2A satellite images and machine learning algorithms for this purpose.…”

Machine learning algorithms for lithological mapping using Sentinel-2 and SRTM DEM in highly vegetated areas

“…It has significant advantages with the ability to combine multiple remote sensing and data sources in lithology mapping as it improves its generalization ability by randomly selecting input or input combinations at each node (Breiman, 2001). It is especially effective for processing high-dimensional and noisy input data and can overcome the interference of vegetation coverage, thereby improving the accuracy of lithological mapping (Harris and Grunsky, 2015;Bachri et al, 2019). However, caution should be exercised when fine-tuning parameters for optimal outcomes and effectively managing computational expenses, particularly when dealing with substantial datasets.…”

“…An appropriate algorithm is one of the key factors contributing to achieving satisfactory classification results. Machine learning algorithms such as maximum likelihood (ML) (Grebby et al, 2011), partial least squares discriminant analysis (PLSDA) (Lu et al, 2021), support vector machine (SVM) (Othman and Gloaguen, 2014;Bachri et al, 2019), and random forest (RF) (Han et al, 2021) have been extensively used for rock classification in vegetation-covered areas because of the rapid advancement of machine learning. In Grebby's study, airborne multispectral imagery and laser scanning data were used to map rock types in the Troodos ophiolite.…”

Discrimination of Fe-Ni-Laterites from Bauxites Using a Novel Support Vector Machines-Based Methodology on Sentinel-2 Data

“…Machine learning (ML) is rapidly gaining ground in lithology and mineral identification [9][10][11]15,18,[23][24][25][26][27][28]. In particular, the Support Vector Machine (SVM) classification method has been applied to ASTER data for the prediction of gold ore deposits [29,30], for lithological discriminations, and for rock and mineral alteration mapping.…”