Abstract:
Multispectral sensors, along with common and advanced algorithms, have become efficient tools for routine lithological
discrimination and mineral potential mapping. It is with this paradigm in mind that this paper sought to evaluate and
discuss the detection and mapping of magnetite on the Eastern Limb of the Bushveld Complex, using high spectral
resolution multispectral remote sensing imagery and GIS techniques. Despite the wide distribution of magnetite, its
economic importance, and its potential as an indicator of many important geological processes, not many studies
had looked at the detection and exploration of magnetite using remote sensing in this region. The Maximum
Likelihood and Support Vector Machine classification algorithms were assessed for their respective ability to detect
and map magnetite using the PlanetScope Analytic data. A K-fold cross-validation analysis was used to measure the
performance of the training as well as the test data. For each classification algorithm, a thematic landcover map was
created and an error matrix, depicting the user’s and producer’s accuracies as well as kappa statistics, was derived.
A pairwise comparison test of the image classification algorithms was conducted to determine whether the two
classification algorithms were significantly different from each other. The Maximum Likelihood Classifier significantly
outperformed the Support Vector Machine algorithm, achieving an overall classification accuracy of 84.58% and an
overall kappa value of 0.79. Magnetite was accurately discriminated from the other thematic landcover classes with a
user’s accuracy of 76.41% and a producer’s accuracy of 88.66%. The overall results of this study illustrated that remote
sensing techniques are effective instruments for geological mapping and mineral investigation, especially iron oxide
mineralization in the Eastern Limb of the Bushveld Complex.
