Comparison of Supervised Classification Methods for Efficiently Locating Possible Mineral Deposits using Multispectral Remote Sensing data
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Abstract
Coastal landforms characterized by an accumulation of a wide range of sediment types and by many varied coastal environments.
Sediment deposits around the central Tamilnadu coast contain significant amounts of heavy minerals and may attain concentrations of economic
importance. The research work emphasize to locating possible heavy mineral deposits from multispectral imagery using supervised classification
methods. We focused on soil prototype for locating possible minerals due to presence of placer deposits and absence of rock formations in our
study area. The textural features were employed aiming at obtaining a highly separable class sets. Many supervised classification techniques are
utilized in surface mineral investigation. Among them, several supervised techniques are analysed and the algorithm that best suit for the
application is determined.
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Keywords: Supervised classification, Multispectral image, Remote sensing, Textural features, Heavy minerals, Band ratios
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