Oil Slick Volume Estimation from Combined Use of Airborne Hyperspectral and Pool Experiment Data

Abstract

To date, estimating oil thickness on the sea surface remains a challenge in most cases. When oil thickness estimation using optical data is limited by the absorption properties of the target, a solution consists in combining experimental and airborne hyperspectral data. We developed a method to identify thickness classes from hyperspectral data which, combined with realistic thickness values derived from a pool experiment, allows to estimate slick volume. Hyperspectral images of the same oil emulsion were acquired over a pool and at sea, under real conditions. From the pool data, we derived two classes: the sheen and the thick pixels, along with their respective thickness. These classes are then identified on the airborne images acquired during the NOFO campaign by generating a detection mask and using two classification approaches based on spectral indices. The proposed method allows to correctly identify the two thickness classes and, combined with the data from the pool experiment, provides a total slick volume larger than the one derived for the Bonn Agreement Oil Appearance Code.

In Situ Microwave Sensors and Switching Circuit for Oil Slick Thickness Measurement

“…Thick and thin oil slick were determined in [6] using a multisource image processing system capable of processing optical, synthetic-aperture radar (SAR) and polarimetric SAR (PolSAR) data. Oil slick volume was estimated by combining airborne hyperspectral and pool experiment data in [7]. Two HySpex spectral cameras mounted on an aircraft captured the data during a clean-up exercise in the North Sea in 2015.…”

Development of the Artificial Intelligence and Optical Sensing Methods for Oil Pollution Monitoring of the Sea by Drones

“…All possible monitoring methods that exist to date [4][5][6][7][8][9][10][11], as well as the capabilities of drones that can be used as carriers of hardware and software monitoring systems, were analyzed, thus allowing the determination of methods used to develop a hardware and software monitoring framework for drones. The method of computer vision was used to monitor the slicks of oil products on the sea surface; thus, slicks were the objects of detection and classification.…”