GRSG Conference 2022: Orbit to Outcrop
Title: Geological and mineralogical mapping in Moroccan central Jebilet using multispectral and hyperspectral satellite data and machine learning
Author: Youssef Najih
The Central Jebilet Massif is one of the main Palaeozoic outcrops in Morocco. This massif is characterized by its arid climate, its significant mining potential and the absence of plant cover, which favors the use of spatial remote sensing for geological mapping and mineral prospecting in this site. The objective of this study is the comparison of hyperspectral data from the Hyperion sensor of the Earth Observing-1 (EO-1) satellite and multispectral data from the Operational Land Imager (OLI) sensor of Landsat 8 in the discrimination of geological units and detection of iron caps in the study area.
The classification by the Support Vector Machine (SVM) method allowed for a good mapping of the lithological units in the study area. The accuracy of the SVM classification of hyperspectral data is higher than that of multispectral data, which was demonstrated by the confusion matrix, notably an overall accuracy of 93.05% and 89.24%, respectively, and a kappa coefficient of 91.25% and 84.36%, respectively.
Concerning the iron detection, the band rationing using both sensors have demonstrated a performance of detecting areas that contain more iron ores, especially, the iron caps of Kettara mine, with a small advantage of hyperspectral data. In overall, our results highlight the efficiency of machine learning classifier and hyperspectral data for the detection of iron ores and the discrimination of lithological units in arid regions. The use of hyperspectral and multispectral images has been shown to be a good technique for the characterization of iron deposits and lithological units, which may help in in mineral exploration engineering with reduced fieldwork and geochemistry.