Mineral exploration by decision tree classification using multi temporal cluster images in Jharkhand region
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India, being an abundant source of minerals, Mineral exploration, on a large scale, is promising to provide good impact for the future of the country. India has a rich source of coal, bauxite, limestone etc. With the advent of remote sensing technologies capturing broader area, exploration of minerals has now become an appreciable problem. Satellite Cluster Images spanning over wider areas can effectively serve as a tool for mapping minerals, qualitatively and quantitatively. In this paper, an algorithm, based on decision tree classification, is developed to map minerals, specifically, coal and limestone for a specific region. Multi-temporal cluster images are employed to map dynamic change detection resulting in greater accuracy. In this paper, multi-temporal cluster images (Landsat 8 OLI/TIRS) are analyzed to map coal, limestone and ‘no-mineral’ regions with the help of the algorithm developed using decision tree classification. Classification results are compared with ground truth data for assessing its accuracy.
KeywordsMulti-temporal cluster images Decision tree classification Landsat 8 Mineral exploration
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