Comparison of Landsat and ASTER in Land Cover Change Detection within Granite Quarries

Moeletsi, R. S.; Tesfamichael, Solomon G.

doi:10.5220/0006675801870195

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…The Maximum Likelihood and the Support Vector Machine classification algorithms both attained a Kappa value above 70%, with an overall accuracy of over 80%, which is a result akin to that found by studies conducted by He et al (2015); Karan and Samadder (2016); Moeletsi and Tesfamichael (2018); Mondal et al (2020). However, it must be noted that the overall accuracy of the Support Vector Machine classification algorithm was relatively high due to the fact that the majority of non-geological classes with the highest number of pixels were the pixels that had the highest accuracies (e.g.…”

Section: Discussionsupporting

confidence: 74%

Detection of magnetite in the Roossenekal area of the Eastern Bushveld Complex, South Africa, using multispectral remote sensing data

Twala

Roberts

Munghemezulu

2020

South African Journal of Geology

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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.

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Section: Discussionsupporting

confidence: 74%