Automated mapping of regolith units with support vector machine and artificial neural network using data from Landsat-8 OLI, ALOS PALSAR DEM, and Sentinel-1A radar images: the case of the Sissingué Gold Project, Northern Côte d’Ivoire
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Date
2023
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International Journal of remote sensing
Abstract
The Sissingué Gold Project is in Northern Côte d’Ivoire and part
of the southern extension, a large peneplain of the landscape in
South Mali. The area has experienced extensive weathering and
erosion over a long time, resulting in a widespread and complex
regolith cover. This diverse and vast regolith cover poses con siderable difficulties for exploration. To overcome this problem,
we adopted mapping of regolith units using machine learning
(ML) with support vector machine (SVM) and artificial neural
network (ANN) algorithms. The main objectives were to (1)
make the predictive regolith map of the study region using
these algorithms with data obtained from Landsat-8 operational
land imager, Advanced Land Observing Satellite Phased Array
Type L-Band Synthetic Aperture Radar digital elevation model
and Sentinel-1A; (2) show the method of pre-processing and
processing the required data and (3) develop the regolith land form unit (RLU) map from the predictive regolith map and col lected data based on the Relict – Erosional – Depositional model.
Tests using the SVM and ANN algorithms showed that both ML
tools could accurately map regolith landforms. An innovative
method of optimizing data using parameters is presented. The
results showed that ANN outperformed SVM with an overall
accuracy of 87.01% and a kappa coefficient of 0.84, whereas
the corresponding values for SVM were 86.69% and 0.84, respec tively. However, the validation data obtained from SVM-based
prediction exhibited a better score than that of validation data
obtained from ANN-based prediction. The Sentinel-1A radar band
combined with Landsat-8 data reduced the vegetation-masking
effect and improved the classification results. The RLUs of this
area are composed primarily of relicts at 24.91%, including lateri tic residuum and soil, depositional material at 36.39% with exotic sediments and ferrierite and the remaining 38.7% of erosional
material made of saprolite, colluvial fragments, and mottled
zones on flanks.
Description
Research Article
Keywords
Support vector machines, neural networks;, remote sensing