Oil spill classification using an autoencoder and hyperspectral technology

Hyperspectral technology has been playing a leading role in monitoring oil spills in marine environments, an issue of international concern. In the case of monitoring oil spills in local areas, hyperspectral technology of small dimensions becomes the ideal solution. This research explores the use of encoded hyperspectral signatures to develop automated classifiers capable of discriminating between polluted and clean water, and even distinguishing between various types of oil. The overall objective is to leverage these classifiers to be able to improve the performance of conventional systems that rely solely on hyperspectral imagery. The acquisition of the hyperspectral signatures of water and hydrocarbons was carried out with a spectroradiometer. The range of the spectroradiometer used in this study covers the ranges between [350-1000] (visible near-infrared) and [1000-2500] (short-wavelength infrared). This gives detailed information with regards to the targets of interest. Different neural autoencoders (AEs) have been developed to reduce inputs into different dimensions, from 1 to 15. Each of these encoded sets was used to train decision tree (DT) classifiers. The results are very promising, as they show that AEs performance encoded data with correlation coefficients above 0.95. The classifiers trained with the different sets provide accuracies close to 1.