Development of a remote sensing system for detection and classification of oil spills using laser fluorosensor

Abstract

This paper focuses on the development of a remote sensing system for detection and classification of oil spills using laser
fluorosensor. The slow response and intervention by the oil spill monitoring team over the years in Nigeria is due to the fact that
oil spills are often detected very late and also the difficulty in making decision on the type of instruments to be deployed during
clean-up. Early detection of oil spills and quick interventions are key elements in reducing this menace caused by oil spills in our
environment. In this research work, oil spills classification system based on laser fluorosensor spectra data was modeled and
simulated. Artificial Neural Network (ANN) toolbox in Matlab/Simulink with MLP (multi-layer perceptron) based supervised
architecture was used for the simulation. Using the data in form of 90-channel spectra as inputs, the ANN presents the analysis
and estimation results of oil products and various background materials as outputs. The network was trained to understand
numerous spectra data of laser fluorosensor for different oil spill products (light oil, medium oil, and heavy oil) and other
backgrounds (water, sand and stone). The trained network was tested using data set to the network. A back propagation learning
algorithm with an optimizer based on gradient descent method was used during the training of the network. It was found that the
ANN with MLP based supervised architecture performed well when the number of neurons in hidden layers is the same and an
average of 100% classification result was achieved. It was also found that Laser fluorosensor must be operated at wavelength
between 302nm and 340nm to produce well- distinguished fluorescence spectra.