Comparison of interaction supervised classification methods with indices to identify areas contaminated with hydrocarbons

Abstract Field companies and laboratories impose certain controls to preserve the environment, and it has recently been proven that oil facilities are among the main causes of pollution in the environment due to non-compliance with environmental safety instructions and the quantities of crude oil extracted on a daily basis. In this study, some of the remote sensing techniques were used, which is interaction supervised classification by relying on data Real field samples. The study area was classified based on dividing the study area into five regions. To determine the effective ranges for detecting hydrocarbon pollution, it will apply four indices to find out which of them gives better results, based on the range of the wavelength that response to the hydrocarbon components. The image from Landsat were downloaded at the same date of collecting the samples. Adaptive methods were generated along with the four indices and evaluated through an assessment’s methods. The adaptive method gives a magnificent result to be used in detecting hydrocarbon contamination.