Study on the Potential of Oil Spill Monitoring in a Port Environment Using Optical Reflectance

In this work, we studied the potential of the visible, near-infrared, and shortwave infrared wavelength regions for monitoring oil spill incidents using optical reflectance. First, a simple physical model was designed for accurate oil thickness and volume estimation using optical reflectance. The developed method was made invariant to changes in acquisition and illumination conditions. In the next step, an algorithm based on an artificial neural network was designed to detect spilled oil. The training samples that are required to optimize the parameters of the network were generated by utilizing the proposed physical model. To validate the method, experiments were conducted in laboratory and outdoor scenarios for detection and thickness/volume estimation on four different oil types. In particular, we developed hyperspectral datasets of oil samples with varying thickness between 500 mu m and 5000 mu m acquired using two different sensors, an Agrispec spectrometer and an Imec snapscan shortwave infrared hyperspectral camera, in strictly controlled experimental settings. To demonstrate the potential of the proposed method in outdoor environments using solely the visible wavelength region, we monitored the evolution of artificially spilled oil in an outdoor scene with an RGB camera mounted on a drone.

Automated summary

This study investigates the potential of using optical reflectance in the visible, near-infrared, and shortwave infrared wavelength regions for monitoring oil spills. A physical model and an artificial neural network algorithm were developed to accurately estimate oil thickness and volume, while maintaining stability under changing conditions. Experimental validation was conducted on different types of oil, and the results demonstrate the effectiveness of the proposed method.