Abstract
Machine Learning Algorithms (MLAs) have recently introduced considerable lithologic mapping. Thus, this study scrutinizes the efficacy of Artificial Neural Network (ANN), Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM) over hybrid datasets including optical (Sentinel 2, ASTER, Landsat OLI and Earth-observing 1 Advanced Land Imager (ALI)), radar (Sentinel 1 and ALOS PALSAR), DEMs and their derivatives (Slope, and Aspect). The study aims to (1) monitor the effect of data dimensionality in enhancing categorization accuracy. (2) disclose the most efficient MLA and most powerful dataset in labeling rock units accurately. (3) highlight the impact of embedding topographical and radar data in lithologic classification. (4) outline the best relation between the number of training pixels and number of utilized bands, in delivering reliable allocation. To achieve these aims, we selected training and testing pixels meticulously, in concordance with a recently published geological map of the study area. We adopted a stacked vector approach for handling the implemented multi-sensor data. Results show that diversifying information sources raised the classification accuracy by approximately 10% for each classifier. SVM and MLC are much better than ANN. Slope is better than aspect and both are less qualified when compared to DEM. Sentinel 1 (C-band) and ALOS PALSAR (L-band) effects are not so different whatever the implemented polarization. Landsat OLI is less qualified in lithologic classification when compared to Sentinel 2, ASTER and ALI. The utilized training pixels should be at least 30N for (N) channels submitted to the classifiers.
Highlights
Accurate geological mapping is the key for economic minerals localization and geo-hazards mitigations
The latter clearly shows the notability of Support Vector Machine (SVM) and Maximum Likelihood Classifier (MLC) over Artificial Neural Network (ANN), and obviously, confirm the effect of data dimensionality in boosting classi fication accuracy
SVM performs effective classification with 85% accuracy SVM performs effective classification with 88.36% accuracy the effectiveness of Sentinel 2A data and SVM is better than MLC SVM is better than random forest classifier (RF) SVM is better than MLC which is in turn better than ANN, DEM enhances the classification, S2, ASTER and Advanced Land Imager (ALI) are preferred when compared to Landsat OLI, Integration of multi-sensors strongly boost the outputs
Summary
Accurate geological mapping is the key for economic minerals localization and geo-hazards mitigations. On the other hand and for datasets, previous studies highly recommend using ASTER in lithologic mapping (Jellouli et al, 2016; Othman and Gloaguen, 2014, 2017; Yu et al, 2012), others (Bentahar and Raji, 2021; Fatima et al, 2013; He et al, 2015) utilized Landsat data effectively. With the advent of the recently launched Sentinel 2 (S2) data, several studies highlighted the advantages of S2 in categorization rock units due to its higher spectral resolutions (Bentahar and Raji, 2021; Ge et al, 2018). For the first time, this study implied the four widely used sensors in lithologic mapping
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